International Journal of Wavelets, Multiresolution and Information Processing Vol. 17, No. 1 (2019) 1950003 (27 pages) c  World Scientific Publishing Company DOI: 10.1142/S0219691319500036 On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation Apurba Roy Department of Information Technology College of Engineering and Management, Kolaghat Kolaghat, West Bengal 721171, India apurbaroy@cemk.ac.in Santi P. Maity * Department of Information Technology Indian Institute of Engineering Science and Technology, Shibpur Howrah, West Bengal 711103, India santipmaity@it.iiests.ac.in Received 25 November 2015 Revised 1 October 2018 Accepted 1 October 2018 Published 14 November 2018 In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with ran- dom noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works. Keywords : MR images; segmentation; compressed sensing; curvelet transform; fuzzy con- ditional entropy; genetic algorithms. AMS Subject Classification 2010: Primary 94A08; Secondary 68U10 * Corresponding author. 1950003-1 Int. J. Wavelets Multiresolut Inf. Process. 2019.17. Downloaded from www.worldscientific.com by 3.236.55.199 on 06/27/20. Re-use and distribution is strictly not permitted, except for Open Access articles.