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 means, 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 uses the information criteria of Akaike (AIC) and/or Schwarz (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 are studied. The parameters estimated from a simulated phantom are very close to those of the phantom. The determined number of image classes agrees with that of the phantom. The accuracies in determining the number of image classes using AIC and MDL are compared. The MDL criterion performs better than the AIC criterion. A modified MDL shows further improvement.
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