Hyperspectral Imaging (HSI)

Hyperspectral imaging (HSI) also called imaging spectrometer is an emerging imaging modality for medical applications. HSI has been explored for various remote sensing applications by NASA. With the advantages of acquiring two dimensional images across a wide range of electromagnetic spectrum, HSI has been applied to archaeology and art conservation, vegetation and water resource control, food quality and safety control, forensic medicine, crime scene detection, and biomedical areas, etc.

HSI offers great potential for non-invasive disease diagnosis and surgical guidance. Light delivered to the biological tissue undergoes multiple scattering from inhomogeneity of biological structures and absorption primarily in hemoglobin, melanin and water as it propagates through tissue. It is assumed that the absorption, fluorescence and scattering characteristics of tissue change during the progression of disease, therefore the reflected, fluorescent and transmitted light from tissue captured by HSI carries quantitative diagnostic information about tissue pathology. In recent years, the advancements of hyperspectral cameras, image analysis methods and computational power make it possible for many exciting applications in the medical field.

Schematic diagram of a pushbroom hyperspectral imaging system

Comparison between hypercube and RGB image. Hypercube is three dimensional dataset a 2D image on each wavelength. The lower left is the reflectance curve (spectral signature) of a pixel in the image. RGB color image only has three image bands on red, green and blue wavelength respectively. The lower right is the intensity curve of a pixel in the RGB image.

Selected Publications

Ortega S, Halicek M, Fabelo H, Camacho R, Plaza MD, Godtliebsen F, M Callicó G, Fei BW (Corresponding author). Hyperspectral imaging for the detection of glioblastoma tumor cells in H&E slides using convolutional neural networks. Sensors; 20(7):1911.
[PDF]
Zhou X, Ma L, Halicek M, Dormer J, Fei BW (Corresponding author). Development of a new polarized hyperspectral imaging microscope. Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020; 11213(1121308). International Society for Optics and Photonics,
[PDF]
Ma L, Halicek M, Fei BW. In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113171C). International Society for Optics and Photonics,
[PDF]
Halicek M, Dormer JD, Little JV, Chen AY, Fei BW (Corresponding author). Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomedical Optics Express; 11(3):1383-400.
[PDF]

Ortega S, Halicek M, Fabelo H, Guerra R, Lopez C, Lejeune M, Godtliebsen F, Callico GM, Fei BW (Corresponding author). Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images. Medical Imaging 2020: Digital Pathology; 11320(113200V). International Society for Optics and Photonics,

[PDF]

Halicek M, Ortega S, Fabelo H, Lopez C, Lejeune M, Callico GM, Fei BW (Corresponding author). Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology. Medical Imaging 2020: Digital Pathology; 11320(113200U). International Society for Optics and Photonics,

[PDF]

Ma L, Halicek M, Zhou X, Dormer J, Fei BW (Corresponding author). Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning. Medical Imaging 2020: Digital Pathology; 11320(113200W). International Society for Optics and Photonics,

[PDF]

Huang J, Halicek M, Shahedi M, Fei BW (Corresponding author). Augmented reality visualization of hyperspectral imaging classifications for image-guided brain tumor resection. Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 11315(113150U). International Society for Optics and Photonics,

[PDF]

Ortega S, Halicek M, Fabelo H, Callico GM, Fei BW (Corresponding author). Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review. Biomedical Optics Express; 11(6): 3195-3233.

[PDF]

Fei BW. Hyperspectral imaging in medical applications. Data Handling in Science and Technology; 32: 523-565. Elsevier,

Halicek M, Himar F, Ortega S, Little JV, Wang X, Chen AY, Callicó GM, Myers LL, Sumer BD, Fei BW (Corresponding author). Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. Proceedings of SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling.

[PDF]

Fabelo H, Halicek M, Ortega S, Shahedi M, Szolna A, Pineiro JF, Sosa C, O’Shanahan AJ, Bisshopp S, Espino C, Marquez M, Hernandez M, Carrera D, Morera J, Callicó GM, Sarmiento R, Fei BW (Corresponding author). Deep learning-based framework for in-vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors;19(4).

[PDF]

Halicek M, Little JV, Wang X, Chen AY, Fei BW (Corresponding author). Optical biopsy of head and neck cancer using hyperspectral imaging and convolution neural networks. Journal of Biomedical Optics;24(3):1-9.

[PDF]

Halicek M, Little JV, Wang X, Chen ZG, Patel M, Griffith CC, El-Diery MW, Sava NF, Chen AY, Fei BW (Corresponding author). Deformable registration of histological cancer margins to gross hyperspectral images using demons. Proceedings of SPIE: The International Society for Optical Engineering;10581.

[PDF]

Halicek M, Little JV, Wang X, Patel M, Griffith CC, El-Diery MW, Chen AY, Fei BW (Corresponding author). Optical biopsy of head and neck cancer using hyperspectral imaging and convolution neural networks. Proceedings of SPIE: The Internation Society for Optical Engineering;10469.

[PDF]

Halicek M, Little JV, Wang X, Patel M, Griffith CC, Chen AY, Fei BW (Corresponding author). Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks. Proceedings of SPIE: The International Society for Optical Engineering;10576.

[PDF]

Halicek, M., Lu, GL., Little, JV., Wang, X., Patel, M., Griffith, CC., El-Deiry, MW., Chen, A., Fei, BW.(2017). “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging.”Journal of Biomedical Optics 22(6): 4.

[PDF]

Ma L, Lu G, Wang D, Wang X, Chen ZG, Fei BW. Deep learning-based classification for head and neck cancer detection with hyperspectral imaging in an animal model, Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372G, March 13, 2017, Orlando, FL.

[PDF]

Lu G, Zhang H, Wang X, Little J, Magliocca K, Chen A, Fei BW (Corresponding author).“Detection of head and neck cancer in surgical specimens using quantitative hyperspectral imaging,” Clinical Cancer Research. 2017 Jun 13. pii: clincanres.0906.2017. doi: 10.1158/1078-0432.CCR-17-0906. [Epub ahead of print] PubMed PMID:28611203.

[PDF] [DOI]

Fei BW, Lu G, Wang X, Zhang H, Little JV, Magliocca KR, Chen AY, Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging, Proc. SPIE 10054, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XV, 100540E, February 14, 2017, San Francisco, CA.

[PDF]

Lu G, Wang D, Qin X, Halig L, Muller S, Zhang H, Chen A, Pogue BW, Chen ZG, Fei BW (Corresponding author). Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery. Journal of Biomedical Optics 2015; 20:126012.

[PDF]

Lu G, Qin X, Wang D, Chen ZG, Fei BW (Corresponding author). Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging. Proceedings of SPIE – The International Society for Optical Engineering 2015; 9415: 94151B. PubMed PMID:26523083.

[PDF]

Pike R, Patton SK, Lu G, Halig LV, Wang D, Chen ZG, Fei BW (Corresponding author). A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection. Proceedings of SPIE – The International Society for Optical Engineering 2014; 9034:90341W. PubMed PMID:25426272.

[PDF]