Abstract
Currently, most pathologists make their diagnosis of cancer based on a rough estimation of the density of the cell's nuclei in the tissue sample, and also based on the morphological abnormality of the cancerous cells. The methods used to achieve their diagnosis are either too simple to diagnose a complicated tissue image or are depending on heavy human intervention and very time consuming. In order to assist pathologists to make a consistent, objective and fast diagnosis, we present in this paper a method of tissue color image segmentation as the main step of an entire system of cancer diagnosis. The segmentation approach is an unsupervised algorithm based on a modified Hopfield neural network (HNN). This algorithm is superior to HNN in the sense that it converges in a prespecified time to a nearby global minimum rather than an early local minimum. Two types of tissue (liver, lung) are presented, and three-color spaces (RGB, HLS and HSV) are used to investigate the efficiency of the algorithm in segmenting color images.