On segmentation of CS reconstructed MR images

This paper addresses the issue of magnetic resonance (MR) Image reconstruction at compressive sampling (or compressed sensing) paradigm followed by its segmentation. To improve image reconstruction problem at low measurement space, weighted linear prediction and random noise injection at unobserved space are done first, followed by spatial domain de-noising through adaptive recursive filtering. Reconstructed image, however, suffers from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform is purposely used for removal of noise and edge enhancement through hard thresholding and suppression of approximate sub-bands, respectively. Finally Genetic algorithms (GAs) based clustering is done for segmentation of sharpen MR Image using weighted contribution of variance and entropy values. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation problems.