@inproceedings{273ba9bed5b748ae84f043c6b1ead829,
title = "A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection",
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.",
keywords = "Hyperspectral imaging, Image classification, Minimum spanning forest, Support vector machine",
author = "Robert Pike and Patton, {Samuel K.} and Guolan Lu and Halig, {Luma V.} and Dongsheng Wang and Chen, {Zhuo Georgia} and Baowei Fei",
year = "2014",
doi = "10.1117/12.2043848",
language = "English (US)",
isbn = "9780819498274",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
booktitle = "Medical Imaging 2014",
note = "Medical Imaging 2014: Image Processing ; Conference date: 16-02-2014 Through 18-02-2014",
}