A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging

作者：Pike Robert; Lu Guolan; Wang Dongsheng; Chen Zhuo Georgia; Fei Baowei^*

来源：IEEE Transactions on Biomedical Engineering, 2016, 63(3): 653-663.

DOI：10.1109/TBME.2015.2468578

• Hyperspectral imaging
• image classification
• minimum spanning forest
• mutual information
• noninvasive cancer detection
• support vector machine

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. AnMSF 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.

• 出版日期2016-3