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Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images.

AUTOMATED BRAIN TUMOR SEGMENTATION IN MR IMAGES USING A HIDDEN MARKOV CLASSIFIER FRAMEWORK TRAINED BY SVD-DERIVED FEATURES