CS Reconstructed MR Image Segmentation Using Morphological Enhancement and FCM

Segmentation of medical images is a very difficult and challenging task due to many inherent complex characteristics present in it. Again 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 with missing lines, edges, shapes, boundaries and curvatures etc. Some preprocessing operation in the form of edge enhancement is essential prior to segmentation operation is applied. Morphological operation namely erosion and dilation may be used in capturing the missing edges, shapes, boundary information etc. In the proposed work first reconstruction of a MR image is done at multi-channel CS platform using weighted fusion rule. Morphological operation is then applied on reconstructed MR images to obtain the detail image, which is then added to the former in spatial domain. Segmentation is then done using FCM clustering. Simulation results are shown to highlight the performance improvement by the proposed method.