21 March 2014 A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection
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Abstract
Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images.
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Robert Pike, Robert Pike, Samuel K. Patton, Samuel K. Patton, Guolan Lu, Guolan Lu, Luma V. Halig, Luma V. Halig, Dongsheng Wang, Dongsheng Wang, Zhuo Georgia Chen, Zhuo Georgia Chen, Baowei Fei, Baowei Fei, } "A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341W (21 March 2014); doi: 10.1117/12.2043848; https://doi.org/10.1117/12.2043848
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