On Maximization of Fuzzy Entropy for MR Image Segmentation at Compressed Sensing

Segmentation of medical images is a very difficult and challenging task due to many inherent complex characteristics present in it. Moreover in many practical situations, medical images are captured at low measurement spaces i.e. at compressed sensing (CS) paradigm for a variety of reasons, for example, due to the limited number of sensors used or measurements may be extremely expensive. Reconstructed medical images after CS operation are found to have uneven intensity values as well as blurred non-uniform shape of the organs. Although conventional discrete wavelets are widely used for edge enhancement and detection, but may not be efficient for detecting the curvatures of the different small organs. Curvelet transform, which is a multi-scale multiresolution transform, can be used as pre-processing for enhancement prior to segmentation of medical images rich with curvatures and missing or broken boundaries. In the proposed work, first reconstruction of a Magnetic Resonance (MR) image is done at multi channel CS platform using a weighted fusion rule. Curvelet transform is then applied on reconstructed MR images to obtain detailed image by suppressing the approximate subband. A sharpen image is developed which is then used for segmentation based on calculating a set of thresholds by maximizing fuzzy entropy for different fuzzy functions. Parameters for the different fuzzy functions that indicate memberships of the different regions, are determined by the Genetic Algorithms (GAs). Extensive simulation results are shown to highlight the performance improvement by the proposed method.