Automatic segmentation of breast masses using enhanced ICA mixture model

Abstract

Image segmentation is considered, among all the stages of image processing, the most critical stage of data processing, because a good classification is dependent on the features extracted from the segmented images. In this work, we are proposing to use the technique called Enhanced ICA Mixture Model (EICAMM) for automatic segmentation of breast masses, aiming to comparing it to other segmentation methods known for segmentation of medical images such as Watershed, Self-Organizing Map (SOM), K-means and Fuzzy C-means techniques. For the analysis of the results, it was used Jaccard similarity measure for comparing the result obtained by the segmentation techniques with that one obtained by an expert opinion. All images considered in this work were segmented and then analyzed by us to improve the segmentation performed by an expert and to detect lesion shape for further classification.These models have been applied for the segmentation of suspicious masses in digital mammographic images, including images of dense breasts. The obtained results show a good performance of EICAMM that was the unique technique able to detect masses in dense breast interest region in preprocessed images and in original images. In this way, EICAMM could be considered as a good alternative approach to be applied for breast masses classification.