Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space

This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing different type of meningioma cancer. The methodpresented to automatically extract the positive cells in meninigioma tumor immunohistochemical pathology images based on HSV color space. First, according to distribution rules of positive cells in the HSV color space, it uses the component H, S and V as threshold conditions and leverages the maximal entropy principle to build a model to segment and extract positive cells. Experimental results shows that proposed algorithm can be used by pathologist to detection reliable quantitatively analyze the parameter of tumor cells and over come to disadvantages of the traditional approach.