A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection

Robert Pike; Samuel K. Patton; Guolan Lu; Luma V. Halig; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei

doi:10.1117/12.2043848

21 March 2014 A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection

Robert Pike, Samuel K. Patton, Guolan Lu, Luma V. Halig, Dongsheng Wang, Zhuo Georgia Chen, Baowei Fei

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90341W (2014) https://doi.org/10.1117/12.2043848
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Robert Pike, Samuel K. Patton, Guolan Lu, Luma V. Halig, Dongsheng Wang, Zhuo Georgia Chen, and 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); https://doi.org/10.1117/12.2043848

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