Automatic color segmentation of breast infrared images using a Gaussian mixture model

Abstract

Primary breast infrared images are provided in gray-level scale. For better visualization and more accurate diagnosis, they are usually converted to pseudo-color version by camera's software or external algorithms. In pseudo colored version different colors represent different temperatures. The segmentation of these images is done with the aim of division of the image to the regions with different temperatures. This is the first step in analyzing the images and will help in detecting tumor region. In this paper the color segmentation is performed using Gaussian mixture model (GMM). In this method weighted Gaussian components are fit to pixel values in RGB color space. Model parameters are estimated using the popular iterative EM algorithm. After segmentation, clusters are ordered with respect to their average temperature increasing. In order to determine the optimum number of clusters, two cluster validity indices named Calinski–Harabasz and Davies–Bouldin are used. Results showed that Davies–Bouldin index has bias toward small number of clusters. Sometimes this may lead to improper classifying of pixels, so Calinski–Harabasz index is preferred. According to the results and subjective evaluation of the specialist radiologist, the proposed method has good performance in separating regions with different temperature and can be used in screening applications.