Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing

A method for parameter estimation in image classification or segmentation is studied within the statistical frame of finite mixture distributions. The method models an image as a finite mixture. Each mixture component corresponds to an image class. Each image class is characterized by parameters, such as the intensity mean, the standard deviation, and the number of image pixels in that class. The method uses a maximum likelihood (ML) approach to estimate the parameters of each class and employs information criteria of Akaike (AIC) and/or Schwarz and Rissanen (MDL) to determine the number of classes in the image. In computing the ML solution of the mixture, the method adopts the expectation maximization (EM) algorithm. The initial estimation and convergence of the ML-EM algorithm were studied. The accuracy in determining the number of image classes using AIC and MDL is compared. The MDL criterion performed better than the AIC criterion. A modified MDL showed further improvement