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Proceedings Paper

A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection
Author(s): Robert Pike; Samuel K. Patton; Guolan Lu; Luma V. Halig; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei
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Paper 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.

Paper Details

Date Published: 21 March 2014
PDF: 8 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90341W (21 March 2014); doi: 10.1117/12.2043848
Show Author Affiliations
Robert Pike, Emory Univ. (United States)
Samuel K. Patton, Emory Univ. (United States)
Guolan Lu, Georgia Institute of Technology (United States)
Emory Univ. (United States)
Luma V. Halig, Emory Univ. (United States)
Dongsheng Wang, Emory Univ. (United States)
Zhuo Georgia Chen, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)
Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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