%0 Journal Article
%A Fei, B.
%A Flask, C.
%A Wang, H.
%A Pi, A.
%A Wilson, D.
%A Shillingford, J.
%A Murcia, N.
%A Weimbs, T.
%A Duerk, J.
%D 2005
%T Image Segmentation, Registration and Visualization of Serial MR Images for Therapeutic Assessment of Polycystic Kidney Disease in Transgenic Mice
%J Conf Proc IEEE Eng Med Biol Soc
%V 1
%P 467-9
%O Image Segmentation, Registration and Visualization of Serial MR Images for Therapeutic Assessment of Polycystic Kidney Disease in Transgenic Mice
%O 1557-170X (Print)
1557-170X (Linking)
%O 10.1109/IEMBS.2005.1616448 [doi]
%O 17282217
%X In vivo small animal imaging provides a powerful tool for the study of a variety of diseases. Magnetic resonance imaging (MRI) has become an established technology for the assessment of therapies. In this study, we used high-resolution MRI to evaluate polycystic kidney disease (PKD) in transgenic mice. We used a customized mouse coil to acquire serial MR images from both wide-type and transgenic PKD mice immediately prior to, and 2-week and 4-week after therapy. We developed image segmentation, registration and visualization methods for this novel imaging application. We measured the kidney volumes for each mouse to assess the efficacy of the therapy. The segmentation results show that the kidney volumes are consistent, which are 348.7 &#177; 19.7 mm<sup>3</sup>for wild-type mice and 756.3 &#177; 44.1 mm<sup>3</sup>for transgenic mice, respectively. The image analysis methods provide a useful tool for this new application.
%O Fei, Baowei
Flask, Chris
Wang, Hesheng
Pi, Ai
Wilson, David
Shillingford, Jonathan
Murcia, Noel
Weimbs, Thomas
Duerk, Jeffrey
R01 CA084433/CA/NCI NIH HHS/United States
R01 DK062338/DK/NIDDK NIH HHS/United States
United States
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Conf Proc IEEE Eng Med Biol Soc. 2005;1:467-9.
%O http://ieeexplore.ieee.org/document/1616448/
%O Department of Radiology, Case Western Reserve University & University Hospitals of Cleveland, USA.
%O Nlm
%O eng

%0 Journal Article
%A Fei, B.
%A Chen, X.
%A Wang, H.
%A Sabol, J. M.
%A DuPont, E.
%A Gilkeson, R. C.
%D 2006
%T Automatic registration of CT volumes and dual-energy digital radiography for detection of cardiac and lung diseases
%J Conf Proc IEEE Eng Med Biol Soc
%V 1
%P 1976-9
%O Automatic registration of CT volumes and dual-energy digital radiography for detection of cardiac and lung diseases
%O 1557-170X (Print)
1557-170X (Linking)
%O 10.1109/IEMBS.2006.259888 [doi]
%O 17945687
%K Algorithms
Artificial Intelligence
Calcinosis/diagnostic imaging
Coronary Angiography/ methods
Coronary Artery Disease/ diagnostic imaging
Humans
Imaging, Three-Dimensional/ methods
Lung Diseases/diagnostic imaging
Radiographic Image Enhancement/ methods
Radiography, Dual-Energy Scanned Projection/ methods
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique
Tomography, X-Ray Computed/ methods
%X We are investigating image processing and analysis techniques to improve the ability of dual-energy digital radiography (DR) for the detection of cardiac calcification. Computed tomography (CT) is an established tool for the diagnosis of coronary artery diseases. Dual-energy digital radiography could be a cost-effective alternative. In this study, we use three-dimensional (3D) CT images as the "gold standard" to evaluate the DR X-ray images for calcification detection. To this purpose, we developed an automatic registration method for 3D CT volumes and two-dimensional (2D) X-ray images. We call this 3D-to-2D registration. We first use a 3D CT image volume to simulate X-ray projection images and then register them with X-ray images. The registered CT projection images are then used to aid the interpretation dual-energy X-ray images for the detection of cardiac calcification. We acquired both CT and X-ray images from patients with coronary artery diseases. Experimental results show that the 3D-to-2D registration is accurate and useful for this new application.
%O Fei, Baowei
Chen, Xiang
Wang, Hesheng
Sabol, John M
DuPont, Elena
Gilkeson, Robert C
R21 CA120536/CA/NCI NIH HHS/United States
R21 CA120536-01/CA/NCI NIH HHS/United States
R21CA120536/CA/NCI NIH HHS/United States
Evaluation Studies
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
United States
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Nihms113523
Conf Proc IEEE Eng Med Biol Soc. 2006;1:1976-9.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743908/pdf/nihms-113523.pdf
%O internal-pdf://0308537105/Fei-2006-Automatic registration of CT volumes.pdf
%O Dept. of Radiol. & Biomed. Eng., Case Western Reserve Univ., Cleveland, OH 44106, USA. baowei.fei@case.edu
%O Nlm
%O eng

%0 Journal Article
%A Chen, X.
%A Gilkeson, R.
%A Fei, B.
%D 2007
%T Automatic Intensity-based 3D-to-2D Registration of CT Volume and Dual-energy Digital Radiography for the Detection of Cardiac Calcification
%J Proc SPIE Int Soc Opt Eng
%V 6512
%O Mar 03
%O Automatic Intensity-based 3D-to-2D Registration of CT Volume and Dual-energy Digital Radiography for the Detection of Cardiac Calcification
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.710192 [doi]
%O 24386527
%X We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DR) for the detection of coronary artery calcification. CT is an established tool for the diagnosis of coronary artery diseases (CADs). Dual-energy digital radiography could be a cost-effective alternative for screening coronary artery calcification. In order to utilize CT as the "gold standard" to evaluate the ability of DR images for the detection and localization of calcium, we developed an automatic intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DR images. To generate digital rendering radiographs (DRR) from the CT volumes, we developed three projection methods, i.e. Gaussian-weighted projection, threshold-based projection, and average-based projection. We tested normalized cross correlation (NCC) and normalized mutual information (NMI) as similarity measurement. We used the Downhill Simplex method as the search strategy. Simulated projection images from CT were fused with the corresponding DR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with mean errors of less 0.8 mm and 0.2 degree for both NCC and NMI. The registration accuracy of the physical phantoms is 0.34 +/- 0.27 mm. Color overlay and 3D visualization of the clinical data show that the two images are registered well. This is consistent with the improvement of the NMI values from 0.20 +/- 0.03 to 0.25 +/- 0.03 after registration. The automatic 3D-to-2D registration method is accurate and robust and may provide a useful tool to evaluate the dual-energy DR images for the detection of coronary artery calcification.
%O Chen, Xiang
Gilkeson, Robert
Fei, Baowei
R21 CA120536/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432054
Proc SPIE Int Soc Opt Eng. 2007 Mar 3;6512. doi: 10.1117/12.710192.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6512/1/Automatic-intensity-based-3D-to-2D-registration-of-CT-volume/10.1117/12.710192.short?SSO=1
%O Case Western Reserve University and Xi'an Jiaotong University.
University Hospitals Case Medical Center.
Case Western Reserve University.
%O Nlm
%O eng

%0 Journal Article
%A Fei, B.
%A Wang, H.
%A Chen, X.
%A Meyers, J.
%A Mulvihill, J.
%A Feyes, D.
%A Edgehouse, N.
%A Duerk, J. L.
%A Pretlow, T. G.
%A Oleinick, N. L.
%D 2007
%T In Vivo Small Animal Imaging for Early Assessment of Therapeutic Efficacy of Photodynamic Therapy for Prostate Cancer
%J Proc SPIE Int Soc Opt Eng
%V 6511
%O Mar 29
%O In Vivo Small Animal Imaging for Early Assessment of Therapeutic Efficacy of Photodynamic Therapy for Prostate Cancer
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.708718 [doi]
%O 24386525
%X We are developing in vivo small animal imaging techniques that can measure early effects of photodynamic therapy (PDT) for prostate cancer. PDT is an emerging therapeutic modality that continues to show promise in the treatment of cancer. At our institution, a new second-generation photosensitizing drug, the silicon phthalocyanine Pc 4, has been developed and evaluated at the Case Comprehensive Cancer Center. In this study, we are developing magnetic resonance imaging (MRI) techniques that provide therapy monitoring and early assessment of tumor response to PDT. We generated human prostate cancer xenografts in athymic nude mice. For the imaging experiments, we used a high-field 9.4-T small animal MR scanner (Bruker Biospec). High-resolution MR images were acquired from the treated and control tumors pre- and post-PDT and 24 hr after PDT. We utilized multi-slice multi-echo (MSME) MR sequences. During imaging acquisitions, the animals were anesthetized with a continuous supply of 2% isoflurane in oxygen and were continuously monitored for respiration and temperature. After imaging experiments, we manually segmented the tumors on each image slice for quantitative image analyses. We computed three-dimensional T2 maps for the tumor regions from the MSME images. We plotted the histograms of the T2 maps for each tumor pre- and post-PDT and 24 hr after PDT. After the imaging and PDT experiments, we dissected the tumor tissues and used the histologic slides to validate the MR images. In this study, six mice with human prostate cancer tumors were imaged and treated at the Case Center for Imaging Research. The T2 values of treated tumors increased by 24 +/- 14% 24 hr after the therapy. The control tumors did not demonstrate significant changes of the T2 values. Inflammation and necrosis were observed within the treated tumors 24 hour after the treatment. Preliminary results show that Pc 4-PDT is effective for the treatment of human prostate cancer in mice. The small animal MR imaging provides a useful tool to evaluate early tumor response to photodynamic therapy in mice.
%O Fei, Baowei
Wang, Hesheng
Chen, Xiang
Meyers, Joseph
Mulvihill, John
Feyes, Denise
Edgehouse, Nancy
Duerk, Jeffrey L
Pretlow, Thomas G
Oleinick, Nancy L
R21 CA120536/CA/NCI NIH HHS/United States
R24 CA110943/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432056
Proc SPIE Int Soc Opt Eng. 2007 Mar 29;6511. doi: 10.1117/12.708718.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6511/1/In-vivo-small-animal-imaging-for-early-assessment-of-therapeutic/10.1117/12.708718.short
%O Department of Radiology, Case Western Reserve University, University Hospitals of Cleveland, and the Case Comprehensive Cancer Center ; Department of Biomedical Engineering, Case Western Reserve University, University Hospitals of Cleveland, and the Case Comprehensive Cancer Center.
Department of Biomedical Engineering, Case Western Reserve University, University Hospitals of Cleveland, and the Case Comprehensive Cancer Center.
Department of Radiology, Case Western Reserve University, University Hospitals of Cleveland, and the Case Comprehensive Cancer Center.
Department of Radiation Oncology, Case Western Reserve University, University Hospitals of Cleveland, and the Case Comprehensive Cancer Center.
Department of Pathology, Case Western Reserve University, University Hospitals of Cleveland, and the Case Comprehensive Cancer Center.
%O Nlm
%O eng

%0 Journal Article
%A Wang, H.
%A Feyes, D.
%A Mulvihill, J.
%A Oleinick, N.
%A Maclennan, G.
%A Fei, B.
%D 2007
%T Multiscale Fuzzy C-Means Image Classification for Multiple Weighted MR Images for the Assessment of Photodynamic Therapy in Mice
%J Proc SPIE Int Soc Opt Eng
%V 6512
%O Mar 08
%O Multiscale Fuzzy C-Means Image Classification for Multiple Weighted MR Images for the Assessment of Photodynamic Therapy in Mice
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.710188 [doi]
%O 24386526
%X We are investigating in vivo small animal imaging and analysis methods for the assessment of photodynamic therapy (PDT), an emerging therapeutic modality for cancer treatment. Multiple weighted MR images were acquired from tumor-bearing mice pre- and post-PDT and 24-hour after PDT. We developed an automatic image classification method to differentiate live, necrotic and intermediate tissues within the treated tumor on the MR images. We used a multiscale diffusion filter to process the MR images before classification. A multiscale fuzzy C-means (FCM) classification method was applied along the scales. The object function of the standard FCM was modified to allow multiscale classification processing where the result from a coarse scale is used to supervise the classification in the next scale. The multiscale fuzzy C-means (MFCM) method takes noise levels and partial volume effects into the classification processing. The method was validated by simulated MR images with various noise levels. For simulated data, the classification method achieved 96.0 +/- 1.1% overlap ratio. For real mouse MR images, the classification results of the treated tumors were validated by histologic images. The overlap ratios were 85.6 +/- 5.1%, 82.4 +/- 7.8% and 80.5 +/- 10.2% for the live, necrotic, and intermediate tissues, respectively. The MR imaging and the MFCM classification methods may provide a useful tool for the assessment of the tumor response to photodynamic therapy in vivo.
%O Wang, Hesheng
Feyes, Denise
Mulvihill, John
Oleinick, Nancy
Maclennan, Gregory
Fei, Baowei
R21 CA120536/CA/NCI NIH HHS/United States
R24 CA110943/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432055
Proc SPIE Int Soc Opt Eng. 2007 Mar 8;6512. doi: 10.1117/12.710188.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6512/1/Multiscale-fuzzy-C-means-image-classification-for-multiple-weighted-MR/10.1117/12.710188.short
%O Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106.
Department of Radiation Oncology, Case Western Reserve University, Cleveland, OH, 44106.
Department of Pathology, Case Western Reserve University, Cleveland, OH, 44106.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106 ; Department of Radiology Case Western Reserve University, Cleveland, OH, 44106.
%O Nlm
%O eng

%0 Journal Article
%A Bebek, O.
%A Hwang, M. J.
%A Fei, B.
%A Cavusoglu, M.
%D 2008
%T Design of a small animal biopsy robot
%J Conf Proc IEEE Eng Med Biol Soc
%V 2008
%P 5601-4
%O Design of a small animal biopsy robot
%O 1557-170X (Print)
1557-170X (Linking)
%O 10.1109/IEMBS.2008.4650484 [doi]
%O 19163987
%K Biopsy/ instrumentation/methods/ veterinary
Equipment Design
Equipment Failure Analysis
Needles/ veterinary
Reproducibility of Results
Robotics/ instrumentation/methods
Sensitivity and Specificity
Surgery, Computer-Assisted/ instrumentation/methods
%X Small animals are widely used in biomedical research studies. They have compact anatomy and small organs. Therefore it is difficult to perceive tumors or cells and perform biopsies manually. Robotics technology offers a convenient and reliable solution for accurate needle insertion. In this paper, a novel 5 degrees of freedom (DOF) robot design for inserting needles into small animal subjects is proposed. The design has a compact size, is light weight, and has high resolution. Parallel mechanisms are used in the design for stable and reliable operation. The proposed robot has two gimbal joints that carry the needle mechanism. The robot can realize dexterous alignment of the needle before insertion.
%O Bebek, Ozkan
Hwang, Myun Joong
Fei, Baowei
Cavusoglu, M
R21 CA120536/CA/NCI NIH HHS/United States
R21 CA120536-01/CA/NCI NIH HHS/United States
R21 CA120536-02/CA/NCI NIH HHS/United States
Evaluation Studies
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
United States
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Nihms113528
Conf Proc IEEE Eng Med Biol Soc. 2008;2008:5601-4. doi: 10.1109/IEMBS.2008.4650484.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796956/pdf/nihms113528.pdf
%O internal-pdf://3037231223/Bebek-2008-Design of a small animal biopsy rob.pdf
%O Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, 44106, USA. oxb6@case.edu
%O Nlm
%O eng

%0 Journal Article
%A Chen, X.
%A Li, K.
%A Gilkeson, R.
%A Fei, B.
%D 2008
%T Gaussian Weighted Projection for Visualization of Cardiac Calcification
%J Proc SPIE Int Soc Opt Eng
%V 6918
%O Mar 15
%O Gaussian Weighted Projection for Visualization of Cardiac Calcification
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.772597 [doi]
%O 24386529
%X At our institution, we are using dual-energy digital radiography (DEDR) as a cost-effective screening tool for the detection of cardiac calcification. We are evaluating DEDR using CT as the gold standard. We are developing image projection methods for the generation of digitally reconstructed radiography (DRR) from CT image volumes. Traditional visualization methods include maximum intensity projection (MIP) and average-based projection (AVG) that have difficulty to show cardiac calcification. Furthermore, MIP can over estimate the calcified lesion as it displays the maximum intensity along the projection rays regardless of tissue types. For AVG projection, the calcified tissue is usually overlapped with bone, lung and mediastinum. In order to improve the visualization of calcification on DRR images, we developed a Gaussian-weighted projection method for this particular application. We assume that the CT intensity values of calcified tissues have a Gaussian distribution. We then use multiple Gaussian functions to fit the intensity histogram. Based on the mean and standard deviation parameters, we incorporate a Gaussian weighted function into the perspective projection and display the calcification exclusively. Our digital and physical phantom studies show that the new projection method can display tissues selectively. In addition, clinical images show that the Gaussian-weighted projection method better visualizes cardiac calcification than either the AVG or MIP method and can be used to evaluate DEDR as a screening tool for the detection of coronary artery diseases.
%O Chen, Xiang
Li, Ke
Gilkeson, Robert
Fei, Baowei
R21 CA120536/CA/NCI NIH HHS/United States
R24 CA110943/CA/NCI NIH HHS/United States
U24 CA110943/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432049
Proc SPIE Int Soc Opt Eng. 2008 Mar 15;6918. doi: 10.1117/12.772597.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6918/1/Gaussian-weighted-projection-for-visualization-of-cardiac-calcification/10.1117/12.772597.short
%O Case Western Reserve University and Xi'an Jiaotong University.
Case Western Reserve University.
University Hospitals Case Medical Center.
%O Nlm
%O eng

%0 Conference Proceedings
%A Li, Ke
%A Fei, Baowei
%D 2008
%T A deformable model-based minimal path segmentation method for kidney MR images
%O Proceedings of SPIE
%I NIH Public Access
%V 6914
%O A deformable model-based minimal path segmentation method for kidney MR images


%0 Journal Article
%A Fei, B.
%A Wang, H.
%A Wu, C.
%A Meyers, J.
%A Xue, L. Y.
%A Maclennan, G.
%A Schluchter, M.
%D 2009
%T Choline Molecular Imaging with Small-animal PET for Monitoring Tumor Cellular Response to Photodynamic Therapy of Cancer
%J Proc SPIE Int Soc Opt Eng
%V 7262
%P 726211
%O Choline Molecular Imaging with Small-animal PET for Monitoring Tumor Cellular Response to Photodynamic Therapy of Cancer
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.812129 [doi]
%O 23336060
%X We are developing and evaluating choline molecular imaging with positron emission tomography (PET) for monitoring tumor response to photodynamic therapy (PDT) in animal models. Human prostate cancer (PC-3) was studied in athymic nude mice. A second-generation photosensitizer Pc 4 was used for PDT in tumor-bearing mice. MicroPET images with (11)C-choline were acquired before PDT and 48 h after PDT. Time-activity curves of (11)C-choline uptake were analyzed before and after PDT. For treated tumors, normalized choline uptake decreased significantly 48 h after PDT, compared to the same tumors pre-PDT (p < 0.001). However, for the control tumors, normalized choline uptake increased significantly (p < 0.001). PET imaging with (11)C-choline is sensitive to detect early tumor response to PDT in the animal model of human prostate cancer.
%O Fei, Baowei
Wang, Hesheng
Wu, Chunying
Meyers, Joseph
Xue, Liang-Yan
Maclennan, Gregory
Schluchter, Mark
R21 CA120536/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432050
Proc SPIE Int Soc Opt Eng. 2009;7262:726211. Epub 2009 Feb 27.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3546344/pdf/nihms432050.pdf
%O internal-pdf://2182586886/Fei-2009-Choline Molecular Imaging with Small-.pdf
%O Department of Radiology, Emory University, Atlanta, GA ; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
%O Nlm
%O eng

%0 Journal Article
%A Fei, B.
%A Yang, X.
%A Wang, H.
%D 2009
%T An MRI-based Attenuation Correction Method for Combined PET/MRI Applications
%J Proc SPIE Int Soc Opt Eng
%V 7262
%O Feb 27
%O An MRI-based Attenuation Correction Method for Combined PET/MRI Applications
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.813755 [doi]
%O 23682307
%X We are developing MRI-based attenuation correction methods for PET images. PET has high sensitivity but relatively low resolution and little anatomic details. MRI can provide excellent anatomical structures with high resolution and high soft tissue contrast. MRI can be used to delineate tumor boundaries and to provide an anatomic reference for PET, thereby improving quantitation of PET data. Combined PET/MRI can offer metabolic, functional and anatomic information and thus can provide a powerful tool to study the mechanism of a variety of diseases. Accurate attenuation correction represents an essential component for the reconstruction of artifact-free, quantitative PET images. Unfortunately, the present design of hybrid PET/MRI does not offer measured attenuation correction using a transmission scan. This problem may be solved by deriving attenuation maps from corresponding anatomic MR images. Our approach combines image registration, classification, and attenuation correction in a single scheme. MR images and the preliminary reconstruction of PET data are first registered using our automatic registration method. MRI images are then classified into different tissue types using our multiscale fuzzy C-mean classification method. The voxels of classified tissue types are assigned theoretical tissue-dependent attenuation coefficients to generate attenuation correction factors. Corrected PET emission data are then reconstructed using a three-dimensional filtered back projection method and an order subset expectation maximization method. Results from simulated images and phantom data demonstrated that our attenuation correction method can improve PET data quantitation and it can be particularly useful for combined PET/MRI applications.
%O Fei, Baowei
Yang, Xiaofeng
Wang, Hesheng
R21 CA120536/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432051
Proc SPIE Int Soc Opt Eng. 2009 Feb 27;7262. pii: 726208.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7262/1/An-MRI-based-attenuation-correction-method-for-combined-PET-MRI/10.1117/12.813755.short
%O Departments of Radiology, Emory University, Atlanta, GA. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
%O Nlm
%O eng

%0 Journal Article
%A Guo, S.
%A Fei, B.
%D 2009
%T A Minimal Path Searching Approach for Active Shape Model (ASM)-based Segmentation of the Lung
%J Proc SPIE Int Soc Opt Eng
%V 7259
%O Mar 27
%O A Minimal Path Searching Approach for Active Shape Model (ASM)-based Segmentation of the Lung
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.812575 [doi]
%O 24386531
%X We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the normal direction. A minimal path (MP) deformable model is applied to drive the searching procedure. A statistical shape prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and manual segmentation is 1.75 +/- 0.33 pixels, while the error is 1.99 +/- 0.45 pixels for the ASM. Our results demonstrate that our ASM-MP method can accurately segment the lung on digital radiographs.
%O Guo, Shengwen
Fei, Baowei
R21 CA120536/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432053
Proc SPIE Int Soc Opt Eng. 2009 Mar 27;7259. doi: 10.1117/12.812575.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7259/1/A-minimal-path-searching-approach-for-active-shape-model-ASM/10.1117/12.812575.short
%O Quantitative BioImaging Laboratory, Department of Radiology, Emory University, Atlanta, GA 30322.
%O Nlm
%O eng

%0 Conference Proceedings
%A Wang, Hesheng
%A Fei, Baowei
%D 2009
%T An MRI-guided PET partial volume correction method
%O Proceedings of SPIE
%I NIH Public Access
%V 7259
%O An MRI-guided PET partial volume correction method


%0 Journal Article
%A Akbari, H.
%A Yang, X.
%A Halig, L. V.
%A Fei, B.
%D 2011
%T 3D Segmentation of Prostate Ultrasound images Using Wavelet Transform
%J Proc SPIE Int Soc Opt Eng
%V 7962
%P 79622K
%O 3D Segmentation of Prostate Ultrasound images Using Wavelet Transform
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.878072 [doi]
%O 22468205
%X The current definitive diagnosis of prostate cancer is transrectal ultrasound (TRUS) guided biopsy. However, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. This paper presents a new method for automatic segmentation of the prostate in three-dimensional transrectal ultrasound images, by extracting texture features and by statistically matching geometrical shape of the prostate. A set of Wavelet-based support vector machines (W-SVMs) are located and trained at different regions of the prostate surface. The WSVMs capture texture priors of ultrasound images for classification of the prostate and non-prostate tissues in different zones around the prostate boundary. In the segmentation procedure, these W-SVMs are trained in three sagittal, coronal, and transverse planes. The pre-trained W-SVMs are employed to tentatively label each voxel around the surface of the model as a prostate or non-prostate voxel by the texture matching. The labeled voxels in three planes after post-processing is overlaid on a prostate probability model. The probability prostate model is created using 10 segmented prostate data. Consequently, each voxel has four labels: sagittal, coronal, and transverse planes and one probability label. By defining a weight function for each labeling in each region, each voxel is labeled as a prostate or non-prostate voxel. Experimental results by using real patient data show the good performance of the proposed model in segmenting the prostate from ultrasound images.
%O Akbari, Hamed
Yang, Xiaofeng
Halig, Luma V
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362788
Proc SPIE Int Soc Opt Eng. 2011;7962:79622K. Epub 2011 Mar 14.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3314427/pdf/nihms362788.pdf
%O internal-pdf://1935685926/Akbari-2011-3D Segmentation of Prostate Ultras.pdf
%O Department of Radiology, Emory University, 1841 Clifton Rd, NE, Atlanta, GA, USA 30329.
%O Nlm
%O eng

%0 Journal Article
%A Yang, X.
%A Akbari, H.
%A Halig, L.
%A Fei, B.
%D 2011
%T 3D Non-rigid Registration Using Surface and Local Salient Features for Transrectal Ultrasound Image-guided Prostate Biopsy
%J Proc SPIE Int Soc Opt Eng
%V 7964
%O Mar 01
%P 79642V
%O 3D Non-rigid Registration Using Surface and Local Salient Features for Transrectal Ultrasound Image-guided Prostate Biopsy
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.878153 [doi]
%O 24027609
%X We present a 3D non-rigid registration algorithm for the potential use in combining PET/CT and transrectal ultrasound (TRUS) images for targeted prostate biopsy. Our registration is a hybrid approach that simultaneously optimizes the similarities from point-based registration and volume matching methods. The 3D registration is obtained by minimizing the distances of corresponding points at the surface and within the prostate and by maximizing the overlap ratio of the bladder neck on both images. The hybrid approach not only capture deformation at the prostate surface and internal landmarks but also the deformation at the bladder neck regions. The registration uses a soft assignment and deterministic annealing process. The correspondences are iteratively established in a fuzzy-to-deterministic approach. B-splines are used to generate a smooth non-rigid spatial transformation. In this study, we tested our registration with pre- and post-biopsy TRUS images of the same patients. Registration accuracy is evaluated using manual defined anatomic landmarks, i.e. calcification. The root-mean-squared (RMS) of the difference image between the reference and floating images was decreased by 62.6+/-9.1% after registration. The mean target registration error (TRE) was 0.88+/-0.16 mm, i.e. less than 3 voxels with a voxel size of 0.38x0.38x0.38 mm3 for all five patients. The experimental results demonstrate the robustness and accuracy of the 3D non-rigid registration algorithm.
%O Yang, Xiaofeng
Akbari, Hamed
Halig, Luma
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362780
Proc SPIE Int Soc Opt Eng. 2011 Mar 1;7964:79642V.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766999/pdf/nihms362780.pdf
%O internal-pdf://0460784861/Yang-2011-3D Non-rigid Registration Using Surf.pdf
%O Department of Radiology, Emory University.
%O Nlm
%O eng

%0 Journal Article
%A Yang, X.
%A Fei, B.
%D 2011
%T A MR Brain Classification Method Based on Multiscale and Multiblock Fuzzy C-means
%J Int Conf Bioinform Biomed Eng
%V 
%P 1-4
%O A MR Brain Classification Method Based on Multiscale and Multiblock Fuzzy C-means
%O 2151-7614 (Print)
2151-7614 (Linking)
%O 23358117
%X A fully automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with intensity correction for MR images is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and reducing the standard deviation of range function. We separate every scale image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels to overcome the effect of intensity inhomogeneity. The method is robust for noise MR images with intensity inhomogeneity because of its multiscale and multiblock bilateral filtering scheme. Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method on synthesized images, simulated brain MR images, and real MR images. The MsbFCM method achieved an overlap ratio of greater than 91% as validated by the ground truth even if original images have 9% noise and 40% intensity inhomogeneity. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images.
%O Yang, Xiaofeng
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering
Nihms432047
Int Conf Bioinform Biomed Eng. 2011:1-4.
%O Department of Radiology, Emory University, Atlanta, GA 30329.
%O Nlm
%O eng

%0 Journal Article
%A Yang, X.
%A Schuster, D.
%A Master, V.
%A Nieh, P.
%A Fenster, A.
%A Fei, B.
%D 2011
%T Automatic 3D Segmentation of Ultrasound Images Using Atlas Registration and Statistical Texture Prior
%J Proc SPIE Int Soc Opt Eng
%V 7964
%O Automatic 3D Segmentation of Ultrasound Images Using Atlas Registration and Statistical Texture Prior
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.877888 [doi]
%O 22708024
%X We are developing a molecular image-directed, 3D ultrasound-guided, targeted biopsy system for improved detection of prostate cancer. In this paper, we propose an automatic 3D segmentation method for transrectal ultrasound (TRUS) images, which is based on multi-atlas registration and statistical texture prior. The atlas database includes registered TRUS images from previous patients and their segmented prostate surfaces. Three orthogonal Gabor filter banks are used to extract texture features from each image in the database. Patient-specific Gabor features from the atlas database are used to train kernel support vector machines (KSVMs) and then to segment the prostate image from a new patient. The segmentation method was tested in TRUS data from 5 patients. The average surface distance between our method and manual segmentation is 1.61 +/- 0.35 mm, indicating that the atlas-based automatic segmentation method works well and could be used for 3D ultrasound-guided prostate biopsy.
%O Yang, Xiaofeng
Schuster, David
Master, Viraj
Nieh, Peter
Fenster, Aaron
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362792
Proc SPIE Int Soc Opt Eng. 2011;7964. pii: 796432. Epub 2011 Mar 1.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7964/1/Automatic-3D-segmentation-of-ultrasound-images-using-atlas-registration-and/10.1117/12.877888.short
%O Department of Radiology, Emory University, Atlanta, GA, USA.
%O Nlm
%O eng

%0 Journal Article
%A Yang, X.
%A Sechopoulos, I.
%A Fei, B.
%D 2011
%T Automatic Tissue Classification for High-resolution Breast CT Images Based on Bilateral Filtering
%J Proc SPIE Int Soc Opt Eng
%V 7962
%O Mar 14
%P 79623H
%O Automatic Tissue Classification for High-resolution Breast CT Images Based on Bilateral Filtering
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.877881 [doi]
%O 24027608
%X Breast tissue classification can provide quantitative measurements of breast composition, density and tissue distribution for diagnosis and identification of high-risk patients. In this study, we present an automatic classification method to classify high-resolution dedicated breast CT images. The breast is classified into skin, fat and glandular tissue. First, we use a multiscale bilateral filter to reduce noise and at the same time keep edges on the images. As skin and glandular tissue have similar CT values in breast CT images, we use morphologic operations to get the mask of the skin based on information of its position. Second, we use a modified fuzzy C-mean classification method twice, one for the skin and the other for the fatty and glandular tissue. We compared our classified results with manually segmentation results and used Dice overlap ratios to evaluate our classification method. We also tested our method using added noise in the images. The overlap ratios for glandular tissue were above 94. 7% for data from five patients. Evaluation results showed that our method is robust and accurate.
%O Yang, Xiaofeng
Sechopoulos, Ioannis
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362783
Proc SPIE Int Soc Opt Eng. 2011 Mar 14;7962:79623H.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766982/pdf/nihms362783.pdf
%O internal-pdf://2836566121/Yang-2011-Automatic Tissue Classification for.pdf
%O Department of Radiology, Emory University.
%O Nlm
%O eng

%0 Journal Article
%A Akbari, H.
%A Halig, L. V.
%A Zhang, H.
%A Wang, D.
%A Chen, Z. G.
%A Fei, B.
%D 2012
%T Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method
%J Proc SPIE Int Soc Opt Eng
%V 8317
%P 831711
%O Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.912026 [doi]
%O 23336061
%X The proposed macroscopic optical histopathology includes a broad-band light source which is selected to illuminate the tissue glass slide of suspicious pathology, and a hyperspectral camera that captures all wavelength bands from 450 to 950 nm. The system has been trained to classify each histologic slide based on predetermined pathology with light having a wavelength within a predetermined range of wavelengths. This technology is able to capture both the spatial and spectral data of tissue. Highly metastatic human head and neck cancer cells were transplanted to nude mice. After 2-3 weeks, the mice were euthanized and the lymph nodes and lung tissues were sent to pathology. The metastatic cancer is studied in lymph nodes and lungs. The pathological slides were imaged using the hyperspectral camera. The results of the proposed method were compared to the pathologic report. Using hyperspectral images, a library of spectral signatures for different tissues was created. The high-dimensional data were classified using a support vector machine (SVM). The spectra are extracted in cancerous and non-cancerous tissues in lymph nodes and lung tissues. The spectral dimension is used as the input of SVM. Twelve glasses are employed for training and evaluation. The leave-one-out cross-validation method is used in the study. After training, the proposed SVM method can detect the metastatic cancer in lung histologic slides with the specificity of 97.7% and the sensitivity of 92.6%, and in lymph node slides with the specificity of 98.3% and the sensitivity of 96.2%. This method may be able to help pathologists to evaluate many histologic slides in a short time.
%O Akbari, Hamed
Halig, Luma V
Zhang, Hongzheng
Wang, Dongsheng
Chen, Zhuo Georgia
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
P50 CA128613/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms432042
Proc SPIE Int Soc Opt Eng. 2012;8317:831711. Epub 2012 Mar 23.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3546351/pdf/nihms432042.pdf
%O internal-pdf://1042028793/Akbari-2012-Detection of Cancer Metastasis Usi.pdf
%O Department of Radiology and Imaging Sciences, Emory University and Georgia Institute of Technology, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Fei, B.
%A Schuster, D. M.
%A Master, V.
%A Akbari, H.
%A Fenster, A.
%A Nieh, P.
%D 2012
%T A Molecular Image-directed, 3D Ultrasound-guided Biopsy System for the Prostate
%J Proc SPIE Int Soc Opt Eng
%V 2012
%O A Molecular Image-directed, 3D Ultrasound-guided Biopsy System for the Prostate
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.912182 [doi]
%O 22708023
%X Systematic transrectal ultrasound (TRUS)-guided biopsy is the standard method for a definitive diagnosis of prostate cancer. However, this biopsy approach uses two-dimensional (2D) ultrasound images to guide biopsy and can miss up to 30% of prostate cancers. We are developing a molecular image-directed, three-dimensional (3D) ultrasound image-guided biopsy system for improved detection of prostate cancer. The system consists of a 3D mechanical localization system and software workstation for image segmentation, registration, and biopsy planning. In order to plan biopsy in a 3D prostate, we developed an automatic segmentation method based wavelet transform. In order to incorporate PET/CT images into ultrasound-guided biopsy, we developed image registration methods to fuse TRUS and PET/CT images. The segmentation method was tested in ten patients with a DICE overlap ratio of 92.4% +/- 1.1 %. The registration method has been tested in phantoms. The biopsy system was tested in prostate phantoms and 3D ultrasound images were acquired from two human patients. We are integrating the system for PET/CT directed, 3D ultrasound-guided, targeted biopsy in human patients.
%O Fei, Baowei
Schuster, David M
Master, Viraj
Akbari, Hamed
Fenster, Aaron
Nieh, Peter
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
P50 CA128613/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362795
Proc SPIE Int Soc Opt Eng. 2012;2012. pii: 831613. Epub 2012 Feb 16.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8316/1/A-molecular-image-directed-3D-ultrasound-guided-biopsy-system-for/10.1117/12.912182.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30329.
%O Nlm
%O eng

%0 Journal Article
%A Yang, X.
%A Fei, B.
%D 2012
%T 3D Prostate Segmentation of Ultrasound Images Combining Longitudinal Image Registration and Machine Learning
%J Proc SPIE Int Soc Opt Eng
%V 8316
%O Feb 23
%P 83162O
%O 3D Prostate Segmentation of Ultrasound Images Combining Longitudinal Image Registration and Machine Learning
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.912188 [doi]
%O 24027622
%X We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 +/- 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.
%O Yang, Xiaofeng
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362793
Proc SPIE Int Soc Opt Eng. 2012 Feb 23;8316:83162O.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767004/pdf/nihms362793.pdf
%O internal-pdf://0172828148/Yang-2012-3D Prostate Segmentation of Ultrasou.pdf
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Yang, X.
%A Ghafourian, P.
%A Sharma, P.
%A Salman, K.
%A Martin, D.
%A Fei, B.
%D 2012
%T Nonrigid Registration and Classification of the Kidneys in 3D Dynamic Contrast Enhanced (DCE) MR Images
%J Proc SPIE Int Soc Opt Eng
%V 8314
%P 83140B
%O Nonrigid Registration and Classification of the Kidneys in 3D Dynamic Contrast Enhanced (DCE) MR Images
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.912190 [doi]
%O 22468206
%X We have applied image analysis methods in the assessment of human kidney perfusion based on 3D dynamic contrast-enhanced (DCE) MRI data. This approach consists of 3D non-rigid image registration of the kidneys and fuzzy C-mean classification of kidney tissues. The proposed registration method reduced motion artifacts in the dynamic images and improved the analysis of kidney compartments (cortex, medulla, and cavities). The dynamic intensity curves show the successive transition of the contrast agent through kidney compartments. The proposed method for motion correction and kidney compartment classification may be used to improve the validity and usefulness of further model-based pharmacokinetic analysis of kidney function.
%O Yang, Xiaofeng
Ghafourian, Pegah
Sharma, Puneet
Salman, Khalil
Martin, Diego
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362796
Proc SPIE Int Soc Opt Eng. 2012;8314:83140B. Epub 2012 Feb 13.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3314431/pdf/nihms362796.pdf
%O internal-pdf://1173117626/Yang-2012-Nonrigid Registration and Classifica.pdf
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Akbari, H.
%A Fei, B.
%D 2013
%T Automatic 3D Segmentation of the Kidney in MR Images Using Wavelet Feature Extraction and Probability Shape Model
%J Proc SPIE Int Soc Opt Eng
%V 8314
%O Feb 23
%P 83143D
%O Automatic 3D Segmentation of the Kidney in MR Images Using Wavelet Feature Extraction and Probability Shape Model
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.912028 [doi]
%O 24027620
%X Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching of geometrical shape of the kidney. A set of Wavelet-based support vector machines (W-SVMs) is trained on the MR images. The W-SVMs capture texture priors of MRI for classification of the kidney and non-kidney tissues in different zones around the kidney boundary. In the segmentation procedure, these W-SVMs are trained to tentatively label each voxel around the kidney model as a kidney or non-kidney voxel by texture matching. A probability kidney model is created using 10 segmented MRI data. The model is initially localized based on the intensity profiles in three directions. The weight functions are defined for each labeled voxel for each Wavelet-based, intensity-based, and model-based label. Consequently, each voxel has three labels and three weights for the Wavelet feature, intensity, and probability model. Using a 3D edge detection method, the model is re-localized and the segmented kidney is modified based on a region growing method in the model region. The probability model is re-localized based on the results and this loop continues until the segmentation converges. Experimental results with mouse MRI data show the good performance of the proposed method in segmenting the kidney in MR images.
%O Akbari, Hamed
Fei, Baowei
R01 CA156775-02/CA/NCI NIH HHS/United States
R01 CA156775-01/CA/NCI NIH HHS/United States
R01 CA156775-03/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
UL1 RR025008/RR/NCRR NIH HHS/United States
P50 CA128301/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms362797
Proc SPIE Int Soc Opt Eng. 2013 Feb 23;8314:83143D.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766988/pdf/nihms-362797.pdf
%O internal-pdf://2392950519/Akbari-2013-Automatic 3D Segmentation of the K.pdf
%O Department of Radiology and Imaging Sciences, Emory University and Georgia Institute of Technology, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Halig, L. V.
%A Wang, D.
%A Wang, A. Y.
%A Chen, Z. G.
%A Fei, B.
%D 2013
%T Biodistribution Study of Nanoparticle Encapsulated Photodynamic Therapy Drugs Using Multispectral Imaging
%J Proc SPIE Int Soc Opt Eng
%V 8672
%O Mar 29
%O Biodistribution Study of Nanoparticle Encapsulated Photodynamic Therapy Drugs Using Multispectral Imaging
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2006492 [doi]
%O 24236230
%X Photodynamictherapy (PDT) uses a drug called a photosensitizer that is excited by irradiation with a laser light of a particular wavelength, which generates reactive singlet oxygen that damages the tumor cells. The photosensitizer and light are inert; therefore, systemic toxicities are minimized in PDT. The synthesis of novel PDT drugs and the use of nanosized carriers for photosensitizers may improve the efficiency of the therapy and the delivery of the drug. In this study, we formulated two nanoparticles with and without a targeting ligand to encapsulate phthalocyanines 4 (Pc 4) molecule and compared their biodistributions. Metastatic human head and neck cancer cells (M4e) were transplanted into nude mice. After 2-3 weeks, the mice were injected with Pc 4, Pc 4 encapsulated into surface coated iron oxide (IO-Pc 4), and IO-Pc 4 conjugated with a fibronectin-mimetic peptide (FMP-IO-Pc 4) which binds specifically to integrin beta1. The mice were imaged using a multispectral camera. Using multispectral images, a library of spectral signatures was created and the signal per pixel of each tumor was calculated, in a grayscale representation of the unmixed signal of each drug. An enhanced biodistribution of nanoparticle encapsulated PDT drugs compared to non-formulated Pc 4 was observed. Furthermore, specific targeted nanoparticles encapsulated Pc 4 has a quicker delivery time and accumulation in tumor tissue than the non-targeted nanoparticles. The nanoparticle-encapsulated PDT drug can have a variety of potential applications in cancer imaging and treatment.
%O Halig, Luma V
Wang, Dongsheng
Wang, Andrew Y
Chen, Zhuo Georgia
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA120536/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms514146
Proc SPIE Int Soc Opt Eng. 2013 Mar 29;8672. doi: 10.1117/12.2006492.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8672/1/Biodistribution-study-of-nanoparticle-encapsulated-photodynamic-therapy-drugs-using-multispectral/10.1117/12.2006492.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Qin, X.
%A Cong, Z.
%A Halig, L. V.
%A Fei, B.
%D 2013
%T Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set
%J Proc SPIE Int Soc Opt Eng
%V 8669
%O Mar 13
%O Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2006490 [doi]
%O 24236228
%X An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%+/-2.3% and 83.6+/-7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.
%O Qin, Xulei
Cong, Zhibin
Halig, Luma V
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms514138
Proc SPIE Int Soc Opt Eng. 2013 Mar 13;8669. doi: 10.1117/12.2006490.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8669/1/Automatic-segmentation-of-right-ventricle-on-ultrasound-images-using-sparse/10.1117/12.2006490.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Qin, X.
%A Cong, Z.
%A Jiang, R.
%A Shen, M.
%A Wagner, M. B.
%A Kishbom, P.
%A Fei, B.
%D 2013
%T Extracting Cardiac Myofiber Orientations from High Frequency Ultrasound Images
%J Proc SPIE Int Soc Opt Eng
%V 8675
%O Mar 29
%O Extracting Cardiac Myofiber Orientations from High Frequency Ultrasound Images
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2006494 [doi]
%O 24392208
%X Cardiac myofiber plays an important role in stress mechanism during heart beating periods. The orientation of myofibers decides the effects of the stress distribution and the whole heart deformation. It is important to image and quantitatively extract these orientations for understanding the cardiac physiological and pathological mechanism and for diagnosis of chronic diseases. Ultrasound has been wildly used in cardiac diagnosis because of its ability of performing dynamic and noninvasive imaging and because of its low cost. An extraction method is proposed to automatically detect the cardiac myofiber orientations from high frequency ultrasound images. First, heart walls containing myofibers are imaged by B-mode high frequency (>20 MHz) ultrasound imaging. Second, myofiber orientations are extracted from ultrasound images using the proposed method that combines a nonlinear anisotropic diffusion filter, Canny edge detector, Hough transform, and K-means clustering. This method is validated by the results of ultrasound data from phantoms and pig hearts.
%O Qin, Xulei
Cong, Zhibin
Jiang, Rong
Shen, Ming
Wagner, Mary B
Kishbom, Paul
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms514141
Proc SPIE Int Soc Opt Eng. 2013 Mar 29;8675. doi: 10.1117/12.2006494.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8675/1/Extracting-cardiac-myofiber-orientations-from-high-frequency-ultrasound-images/10.1117/12.2006494.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Pediatrics, Emory University School of Medicine, Atlanta, GA.
Department of Surgery, Emory University School of Medicine, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA ; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology ; Department of Mathematics & Computer Science, Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Conference Proceedings
%A Wooten III, Walter J
%A Nye, Jonathan A
%A Schuster, David M
%A Nieh, Peter T
%A Master, Viraj A
%A Votaw, John R
%A Fei, Baowei
%D 2013
%T Accuracy evaluation of a 3D ultrasound-guided biopsy system
%O Proceedings of SPIE
%I NIH Public Access
%V 8671
%O Accuracy evaluation of a 3D ultrasound-guided biopsy system


%0 Conference Proceedings
%A Lu, Guolan
%A Halig, Luma
%A Wang, Dongsheng
%A Chen, Zhuo Georgia
%A Fei, Baowei
%D 2014
%T Hyperspectral imaging for cancer surgical margin delineation: registration of hyperspectral and histological images
%O Proceedings of SPIE
%I NIH Public Access
%V 9036
%P 90360S
%O Hyperspectral imaging for cancer surgical margin delineation: registration of hyperspectral and histological images
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201054/pdf/nihms613556.pdf
%O internal-pdf://1635925713/Lu-2014-Hyperspectral imaging for cancer surgi.pdf


%0 Conference Proceedings
%A Lu, Guolan
%A Halig, Luma
%A Wang, Dongsheng
%A Chen, Zhuo Georgia
%A Fei, Baowei
%D 2014
%T Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging
%O Proceedings of SPIE
%I NIH Public Access
%V 9034
%P 903413
%O Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201059/pdf/nihms-613551.pdf
%O internal-pdf://0079515804/Lu-2014-Spectral-spatial classification using.pdf


%0 Conference Proceedings
%A Pike, Robert
%A Patton, Samuel K
%A Lu, Guolan
%A Halig, Luma V
%A Wang, Dongsheng
%A Chen, Zhuo Georgia
%A Fei, Baowei
%D 2014
%T A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection
%O Proceedings of SPIE
%I NIH Public Access
%V 9034
%P 90341W
%O A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4241346/pdf/nihms613555.pdf
%O internal-pdf://1577815265/Pike-2014-A minimum spanning forest based hype.pdf


%0 Journal Article
%A Qin, X.
%A Lu, G.
%A Sechopoulos, I.
%A Fei, B.
%D 2014
%T Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features
%J Proc SPIE Int Soc Opt Eng
%V 9034
%O Mar 21
%P 90341V
%O Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2043828 [doi]
%O 25426271
%X Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.
%O Qin, Xulei
Lu, Guolan
Sechopoulos, Ioannis
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R01 CA163746/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms613554
Proc SPIE Int Soc Opt Eng. 2014 Mar 21;9034:90341V.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4241347/pdf/nihms613554.pdf
%O internal-pdf://3322851385/Qin-2014-Breast Tissue Classification in Digit.pdf
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA ; Winship Cancer Institute, Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA ; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA ; Department of Mathematics & Computer Science, Emory University, Atlanta, GA ; Winship Cancer Institute, Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Qin, X.
%A Wang, S.
%A Shen, M.
%A Zhang, X.
%A Wagner, M. B.
%A Fei, B.
%D 2014
%T Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound
%J Proc SPIE Int Soc Opt Eng
%V 9036
%O Mar 12
%P 90361O
%O Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2043821 [doi]
%O 25328641
%X The orientation of cardiac fibers affects the anatomical, mechanical, and electrophysiological properties of the heart. Although echocardiography is the most common imaging modality in clinical cardiac examination, it can only provide the cardiac geometry or motion information without cardiac fiber orientations. If the patient's cardiac fiber orientations can be mapped to his/her echocardiography images in clinical examinations, it may provide quantitative measures for diagnosis, personalized modeling, and image-guided cardiac therapies. Therefore, this project addresses the feasibility of mapping personalized cardiac fiber orientations to three-dimensional (3D) ultrasound image volumes. First, the geometry of the heart extracted from the MRI is translated to 3D ultrasound by rigid and deformable registration. Deformation fields between both geometries from MRI and ultrasound are obtained after registration. Three different deformable registration methods were utilized for the MRI-ultrasound registration. Finally, the cardiac fiber orientations imaged by DTI are mapped to ultrasound volumes based on the extracted deformation fields. Moreover, this study also demonstrated the ability to simulate electricity activations during the cardiac resynchronization therapy (CRT) process. The proposed method has been validated in two rat hearts and three canine hearts. After MRI/ultrasound image registration, the Dice similarity scores were more than 90% and the corresponding target errors were less than 0.25 mm. This proposed approach can provide cardiac fiber orientations to ultrasound images and can have a variety of potential applications in cardiac imaging.
%O Qin, Xulei
Wang, Silun
Shen, Ming
Zhang, Xiaodong
Wagner, Mary B
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms613557
Proc SPIE Int Soc Opt Eng. 2014 Mar 12;9036:90361O.
%O https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201058/pdf/nihms613557.pdf
%O internal-pdf://0499192623/Qin-2014-Mapping Cardiac Fiber Orientations fr.pdf
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Yerkes National Primate Research Center, Emory University, Atlanta, GA.
Department of Pediatrics, Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA ; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Lu, G.
%A Qin, X.
%A Wang, D.
%A Chen, Z. G.
%A Fei, B.
%D 2015
%T Estimation of Tissue Optical Parameters with Hyperspectral Imaging and Spectral Unmixing
%J Proc SPIE Int Soc Opt Eng
%V 9417
%O Mar 17
%O Estimation of Tissue Optical Parameters with Hyperspectral Imaging and Spectral Unmixing
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2082299 [doi]
%O 26855467
%X Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.
%O Lu, Guolan
Qin, Xulei
Wang, Dongsheng
Chen, Zhuo Georgia
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms716028
Proc SPIE Int Soc Opt Eng. 2015 Mar 17;9417. pii: 94170Q.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9417/1/Estimation-of-tissue-optical-parameters-with-hyperspectral-imaging-and-spectral/10.1117/12.2082299.short
%O The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Hematology and Medical Oncology, Emory University, Atlanta, GA.
The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Mathematics & Computer Science, Emory University, Atlanta, GA; Winship Cancer Institute of Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Lu, G.
%A Qin, X.
%A Wang, D.
%A Chen, Z. G.
%A Fei, B.
%D 2015
%T Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging
%J Proc SPIE Int Soc Opt Eng
%V 9415
%O Feb 21
%O Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2082284 [doi]
%O 26523083
%X Complete surgical removal of tumor tissue is essential for postoperative prognosis after surgery. Intraoperative tumor imaging and visualization are an important step in aiding surgeons to evaluate and resect tumor tissue in real time, thus enabling more complete resection of diseased tissue and better conservation of healthy tissue. As an emerging modality, hyperspectral imaging (HSI) holds great potential for comprehensive and objective intraoperative cancer assessment. In this paper, we explored the possibility of intraoperative tumor detection and visualization during surgery using HSI in the wavelength range of 450 nm - 900 nm in an animal experiment. We proposed a new algorithm for glare removal and cancer detection on surgical hyperspectral images, and detected the tumor margins in five mice with an average sensitivity and specificity of 94.4% and 98.3%, respectively. The hyperspectral imaging and quantification method have the potential to provide an innovative tool for image-guided surgery.
%O Lu, Guolan
Qin, Xulei
Wang, Dongsheng
Chen, Zhuo Georgia
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms716024
Proc SPIE Int Soc Opt Eng. 2015 Feb 21;9415. pii: 94151B. Epub 2015 Mar 18.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9415/1/Quantitative-wavelength-analysis-and-image-classification-for-intraoperative-cancer-diagnosis/10.1117/12.2082284.short
%O The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Hematology and Medical Oncology, Emory University, Atlanta, GA.
The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA ; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA ; Department of Mathematics & Computer Science, Emory University, Atlanta, GA ; Winship Cancer Institute of Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Qin, X.
%A Wang, S.
%A Shen, M.
%A Zhang, X.
%A Lerakis, S.
%A Wagner, M. B.
%A Fei, B.
%D 2015
%T Register cardiac fiber orientations from 3D DTI volume to 2D ultrasound image of rat hearts
%J Proc SPIE Int Soc Opt Eng
%V 9415
%O Mar 18
%O Register cardiac fiber orientations from 3D DTI volume to 2D ultrasound image of rat hearts
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2082317 [doi]
%O 26855466
%X Two-dimensional (2D) ultrasound or echocardiography is one of the most widely used examinations for the diagnosis of cardiac diseases. However, it only supplies the geometric and structural information of the myocardium. In order to supply more detailed microstructure information of the myocardium, this paper proposes a registration method to map cardiac fiber orientations from three-dimensional (3D) magnetic resonance diffusion tensor imaging (MR-DTI) volume to the 2D ultrasound image. It utilizes a 2D/3D intensity based registration procedure including rigid, log-demons, and affine transformations to search the best similar slice from the template volume. After registration, the cardiac fiber orientations are mapped to the 2D ultrasound image via fiber relocations and reorientations. This method was validated by six images of rat hearts ex vivo. The evaluation results indicated that the final Dice similarity coefficient (DSC) achieved more than 90% after geometric registrations; and the inclination angle errors (IAE) between the mapped fiber orientations and the gold standards were less than 15 degree. This method may provide a practical tool for cardiologists to examine cardiac fiber orientations on ultrasound images and have the potential to supply additional information for diagnosis of cardiac diseases.
%O Qin, Xulei
Wang, Silun
Shen, Ming
Zhang, Xiaodong
Lerakis, Stamatios
Wagner, Mary B
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms716026
Proc SPIE Int Soc Opt Eng. 2015 Mar 18;9415. pii: 94152M.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9415/1/Register-cardiac-fiber-orientations-from-3D-DTI-volume-to-2D/10.1117/12.2082317.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Yerkes National Primate Research Center, Emory University, Atlanta, GA.
Division of Cardiology, Department of Medicine, Emory University, Atlanta, GA.
Division of Cardiology, Department of Medicine, Emory University, Atlanta, GA; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Pediatrics, Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA.
%O Nlm
%O eng

%0 Conference Proceedings
%A Qin, Xulei
%A Wang, Silun
%A Shen, Ming
%A Zhang, Xiaodong
%A Lerakis, Stamatios
%A Wagner, Mary B
%A Fei, Baowei
%D 2015
%T 3D in vivo imaging of rat hearts by high frequency ultrasound and its application in myofiber orientation wrapping
%O Proceedings of SPIE--the International Society for Optical Engineering
%I NIH Public Access
%V 9419
%O 3D in vivo imaging of rat hearts by high frequency ultrasound and its application in myofiber orientation wrapping


%0 Journal Article
%A Tian, Z.
%A Liu, L.
%A Fei, B.
%D 2015
%T A supervoxel-based segmentation method for prostate MR images
%J Proc SPIE Int Soc Opt Eng
%V 9413
%O Mar 20
%O A supervoxel-based segmentation method for prostate MR images
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2082255 [doi]
%O 26848206
%X Accurate segmentation of the prostate has many applications in prostate cancer diagnosis and therapy. In this paper, we propose a "Supervoxel" based method for prostate segmentation. The prostate segmentation problem is considered as assigning a label to each supervoxel. An energy function with data and smoothness terms is used to model the labeling process. The data term estimates the likelihood of a supervoxel belongs to the prostate according to a shape feature. The geometric relationship between two neighboring supervoxels is used to construct a smoothness term. A three-dimensional (3D) graph cut method is used to minimize the energy function in order to segment the prostate. A 3D level set is then used to get a smooth surface based on the output of the graph cut. The performance of the proposed segmentation algorithm was evaluated with respect to the manual segmentation ground truth. The experimental results on 12 prostate volumes showed that the proposed algorithm yields a mean Dice similarity coefficient of 86.9%+/-3.2%. The segmentation method can be used not only for the prostate but also for other organs.
%O Tian, Zhiqiang
Liu, LiZhi
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms716021
Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. pii: 941318.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9413/1/A-supervoxel-based-segmentation-method-for-prostate-MR-images/10.1117/12.2082255.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology.
%O Nlm
%O eng

%0 Journal Article
%A Tian, Z.
%A Liu, L.
%A Fei, B.
%D 2015
%T A fully automatic multi-atlas based segmentation method for prostate MR images
%J Proc SPIE Int Soc Opt Eng
%V 9413
%O Mar 20
%O A fully automatic multi-atlas based segmentation method for prostate MR images
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2082229 [doi]
%O 26798187
%X Most of multi-atlas segmentation methods focus on the registration between the full-size volumes of the data set. Although the transformations obtained from these registrations may be accurate for the global field of view of the images, they may not be accurate for the local prostate region. This is because different magnetic resonance (MR) images have different fields of view and may have large anatomical variability around the prostate. To overcome this limitation, we proposed a two-stage prostate segmentation method based on a fully automatic multi-atlas framework, which includes the detection stage i.e. locating the prostate, and the segmentation stage i.e. extracting the prostate. The purpose of the first stage is to find a cuboid that contains the whole prostate as small cubage as possible. In this paper, the cuboid including the prostate is detected by registering atlas edge volumes to the target volume while an edge detection algorithm is applied to every slice in the volumes. At the second stage, the proposed method focuses on the registration in the region of the prostate vicinity, which can improve the accuracy of the prostate segmentation. We evaluated the proposed method on 12 patient MR volumes by performing a leave-one-out study. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are used to quantify the difference between our method and the manual ground truth. The proposed method yielded a DSC of 83.4%+/-4.3%, and a HD of 9.3 mm+/-2.6 mm. The fully automated segmentation method can provide a useful tool in many prostate imaging applications.
%O Tian, Zhiqiang
Liu, LiZhi
Fei, Baowei
P50 CA128301/CA/NCI NIH HHS/United States
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms716023
Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. pii: 941340.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9413/1/A-fully-automatic-multi-atlas-based-segmentation-method-for-prostate/10.1117/12.2082229.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology.
%O Nlm
%O eng

%0 Journal Article
%A Chung, H.
%A Lu, G.
%A Tian, Z.
%A Wang, D.
%A Chen, Z. G.
%A Fei, B.
%D 2016
%T Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging
%J Proc SPIE Int Soc Opt Eng
%V 9788
%O Feb 27
%O Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2216559 [doi]
%O 27656035
%X Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.
%O Chung, Hyunkoo
Lu, Guolan
Tian, Zhiqiang
Wang, Dongsheng
Chen, Zhuo Georgia
Fei, Baowei
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA120536/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms816057
Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9788. pii: 978813. Epub 2016 Mar 29.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9788/1/Superpixel-based-spectral-classification-for-the-detection-of-head-and/10.1117/12.2216559.short
%O The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of Hematology and Medical Oncology, Emory University, Atlanta, GA.
The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Mathematics Computer Science, Emory University, Atlanta, GA; Winship Cancer Institute of Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Conference Proceedings
%A Dormer, James
%A Qin, Xulei
%A Shen, Ming
%A Wang, Silun
%A Zhang, Xiaodong
%A Jiang, Rong
%A Wagner, Mary B
%A Fei, Baowei
%D 2016
%T Determining cardiac fiber orientation using FSL and registered ultrasound/DTI volumes
%O Proceedings of SPIE--the International Society for Optical Engineering
%I NIH Public Access
%V 9790
%O Determining cardiac fiber orientation using FSL and registered ultrasound/DTI volumes


%0 Conference Proceedings
%A Lu, Guolan
%A Qin, Xulei
%A Wang, Dongsheng
%A Muller, Susan
%A Zhang, Hongzheng
%A Chen, Amy
%A Chen, Zhuo Georgia
%A Fei, Baowei
%D 2016
%T Quantitative diagnosis of tongue cancer from histological images in an animal model
%O Proceedings of SPIE--the International Society for Optical Engineering
%I NIH Public Access
%V 9791
%O Quantitative diagnosis of tongue cancer from histological images in an animal model


%0 Conference Proceedings
%A Lu, Guolan
%A Qin, Xulei
%A Wang, Dongsheng
%A Muller, Susan
%A Zhang, Hongzheng
%A Chen, Amy
%A Chen, Zhuo Georgia
%A Fei, Baowei
%D 2016
%T Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis
%O Proceedings of SPIE--the International Society for Optical Engineering
%I NIH Public Access
%V 9788
%O Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis


%0 Journal Article
%A Ma, L.
%A Guo, R.
%A Tian, Z.
%A Venkataraman, R.
%A Sarkar, S.
%A Liu, X.
%A Nieh, P. T.
%A Master, V. V.
%A Schuster, D. M.
%A Fei, B.
%D 2016
%T Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images
%J Proc SPIE Int Soc Opt Eng
%V 9786
%O Feb 27
%O Random Walk Based Segmentation for the Prostate on 3D Transrectal Ultrasound Images
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2216526 [doi]
%O 27660383
%X This paper proposes a new semi-automatic segmentation method for the prostate on 3D transrectal ultrasound images (TRUS) by combining the region and classification information. We use a random walk algorithm to express the region information efficiently and flexibly because it can avoid segmentation leakage and shrinking bias. We further use the decision tree as the classifier to distinguish the prostate from the non-prostate tissue because of its fast speed and superior performance, especially for a binary classification problem. Our segmentation algorithm is initialized with the user roughly marking the prostate and non-prostate points on the mid-gland slice which are fitted into an ellipse for obtaining more points. Based on these fitted seed points, we run the random walk algorithm to segment the prostate on the mid-gland slice. The segmented contour and the information from the decision tree classification are combined to determine the initial seed points for the other slices. The random walk algorithm is then used to segment the prostate on the adjacent slice. We propagate the process until all slices are segmented. The segmentation method was tested in 32 3D transrectal ultrasound images. Manual segmentation by a radiologist serves as the gold standard for the validation. The experimental results show that the proposed method achieved a Dice similarity coefficient of 91.37+/-0.05%. The segmentation method can be applied to 3D ultrasound-guided prostate biopsy and other applications.
%O Ma, Ling
Guo, Rongrong
Tian, Zhiqiang
Venkataraman, Rajesh
Sarkar, Saradwata
Liu, Xiabi
Nieh, Peter T
Master, Viraj V
Schuster, David M
Fei, Baowei
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA120536/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms816060
Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9786. pii: 978607. Epub 2016 Mar 18.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9786/1/Random-walk-based-segmentation-for-the-prostate-on-3D-transrectal/10.1117/12.2216526.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; School of Computer Science, Beijing Institute of Technology, Beijing.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of R&D, Eigen, Grass Valley, CA.
School of Computer Science, Beijing Institute of Technology, Beijing.
Department of Urology, Emory University School of Medicine, Atlanta, GA.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Winship Cancer Institute of Emory University, Atlanta, GA; The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA.
%O Nlm
%O eng

%0 Journal Article
%A Ma, L.
%A Guo, R.
%A Tian, Z.
%A Venkataraman, R.
%A Sarkar, S.
%A Liu, X.
%A Tade, F.
%A Schuster, D. M.
%A Fei, B.
%D 2016
%T Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images
%J Proc SPIE Int Soc Opt Eng
%V 9784
%O Feb 27
%O Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images
%O 0277-786X (Print)
0277-786X (Linking)
%O 10.1117/12.2216255 [doi]
%O 27660382
%X Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.
%O Ma, Ling
Guo, Rongrong
Tian, Zhiqiang
Venkataraman, Rajesh
Sarkar, Saradwata
Liu, Xiabi
Tade, Funmilayo
Schuster, David M
Fei, Baowei
R01 CA156775/CA/NCI NIH HHS/United States
R21 CA120536/CA/NCI NIH HHS/United States
R21 CA176684/CA/NCI NIH HHS/United States
United States
Proceedings of SPIE--the International Society for Optical Engineering
Nihms816059
Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9784. pii: 978427. Epub 2016 Mar 21.
%O https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9784/1/Combining-population-and-patient-specific-characteristics-for-prostate-segmentation-on/10.1117/12.2216255.short
%O Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; School of Computer Science, Beijing Institute of Technology, Beijing.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Department of R&D, Eigen, Grass Valley, CA.
School of Computer Science, Beijing Institute of Technology, Beijing.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Winship Cancer Institute of Emory University, Atlanta, GA; The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA.
%O Nlm
%O eng