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

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.

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. An MSF is finally grown to segment the image using spatial and spectral information.

The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images.

Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.