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A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.
- Source :
- IEEE Transactions on Biomedical Engineering; Mar2016, Vol. 63 Issue 3, p653-663, 11p
- Publication Year :
- 2016
-
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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00189294
- Volume :
- 63
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 113293479
- Full Text :
- https://doi.org/10.1109/TBME.2015.2468578