Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 63, Issue 3, Pages 653-663Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2015.2468578
Keywords
Hyperspectral imaging; image classification; minimum spanning forest; mutual information; noninvasive cancer detection; support vector machine
Categories
Funding
- NIH Grants [R01CA156775, R21CA176684]
- Georgia Research Alliance Distinguished Scientists Award
- Emory SPORE in Head and Neck Cancer [NIH P50CA128613]
- Emory Molecular and Translational Imaging Center [NIH P50CA128301]
- Emory Center for Systems Imaging of the Emory University School of Medicine
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Goal: The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods: An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. AnMSF is finally grown to segment the image using spatial and spectral information. Conclusion: The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance: Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.
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