Reconstruction performances of curvelet transform for magnetic resonance images

Journal of Engineering Research

Rashid Hussain²  ABDUL REHMAN MEMON¹ 

[1] Hamdard University, KarachiG-164 PECH Block-3 Off Khalid Bin Walid RoadKarachi, Pakistan

关键词: Clustering;    curvelet transform;    de-noised image;    image segmentation;    wavelet transform;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: Kuwait University * Academic Publication Council

【 摘 要 】

Reconstruction performances of transforms are deeply associated with imageprocessing, scientific computing and computer vision. This research focuses onthe performance of Curvelet Transform for Magnetic Resonance Images. The mainoutcome of this technique includes the removal of non-homogeneous noise usingCurvelet based de-noising methods. Curvelet Transform belongs to the family ofdirectional Wavelets. Curvelet Transform not only contains translations, dilations butalso the rotations, which can enhance the reconstruction of curve objects. This researchinvolves multi-scale reconstruction of objects with edge discontinuities. Experimentalresults show that Curvelet Transform has superior reconstruction capability for animage with curve objects. Another phase of this research covers the segmentationof de-noised images using Fuzzy C-Means Clustering (FCM) algorithm. The clusterformation in FCM algorithm is based on the Euclidian distance between pixels withsimilar intensities. Experimental results show that segmentation of reconstructedimages is adversely affected by the noise bursts.

【 授权许可】

Unknown