Automation of ROI extraction in hyperspectral breast images.

The extraction of regions-of-interest (ROIs) in hyperspectral images of breast cancer specimens is currently carried out manually or by visual inspection. In order to address the labor-intensive and time-consuming process of the manual extraction of ROIs in hyperspectral images, an algorithm is developed in this paper to automate the extraction process. This is achieved by using a contrast module and a homogeneity module to duplicate the same manual or visual steps that an expert goes through in order to extract ROIs. The success of the automated process is determined by comparing the classification rates of the automated approach with the manual approach in terms of the ability to separate cancer cases from normal cases.