Classification of medical images using context dependent methods
Ted R. Jackson, James R. Brookeman, Michael Merickel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
We are developing a method to automatically classify multispectral medical images using context dependent methods. The model is built with the knowledge that cluster of tissue features will overlap in feature space. The goal is to reduce the classification error that results from this cluster overlap. Initialization of the probability of a pixel belonging to a tissue class can take advantage of a priori class distributions if such knowledge exists. Otherwise, the procedure can resort to modeling each class with a Gaussian distribution. These probabilities can then be iteratively updated using either a relaxation labeling algorithm or a Markov random fields algorithm. Once the model converges, iterations cease and each pixel is classified using the maximum probability for all classes.