Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images

We present an algorithm for segmenting cells in a microscopic image of immunohistologically stained slides from breast cancer based on color contents. The procedure for the approach consists of color categorization using neural network, noise removal and shape simplification using mathematical morphology, and cell size consideration. In order to obtain the more accurate segmentation, we further separate the attached cancer cells with marker-controlled watershed. Results show that cancer cells in the background with heterogeneous intensities and colors are appropriately segmented using the proposed method. In other words, the visualization of segmented cancer cells is in agreement with their perception in the original red-green-blue (RGB) image. In addition, the resistance of the method to intensity and color variations in the background is obtained without compromising of computational speed.