Investigations of Medical Image Segmentation Methods with Inclusion Mathematical Morphological Operations

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Medical image segmentation research is becoming efficient by using mathematical morphological (MM) operators. There are different methods in image segmentation such as supervised and unsupervised segmentations. The MM operators are much effective, in developing a computer aided diagnosis (CAD) system. Medical image such as mammograms, generally they are of low contrast, such that radiologists face difficulties in observing the results. Due to this, diagnosis fails to generate high rate false positives and false negatives. In the proposed work improvement of quality of image segmentation with inclusion of morphological operations with other methods such as watershed transform, fuzzy logic based techniques, curvelets and MRF to detect the masses and calcifications in mammograms. Classification of masses and evaluation of segmentation process are done with artificial neural network and other performance metrics. These methods lead to increase in the accuracy, specificity and sensitivity of mammography and reduce unnecessary biopsies.