Abstract
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. An MSF 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.
Original language | English (US) |
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Article number | 7202847 |
Pages (from-to) | 653-663 |
Number of pages | 11 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 63 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2016 |
Externally published | Yes |
Keywords
- Hyperspectral imaging
- image classification
- minimum spanning forest
- mutual information
- noninvasive cancer detection
- support vector machine
ASJC Scopus subject areas
- Biomedical Engineering