The brain MRI image sparse representation based on the gradient information and the non-symmetry and anti-packing model. (31st October 2017)

• Record Type: Journal Article
• Authors: Liang, Hu; Zhao, Shengrong; Dong, Xiangjun
• Is Part Of: Computer assisted surgery. Volume 22(2017)Supplement 1
• Page Start: 106
• Page End: 112
• Publication Date: 2017-10-31
• Subjects: Brain MRI image -- sparse representation -- gradient information -- non-symmetry and anti-packing model; Computer-assisted surgery -- Periodicals
• DOI: 10.1080/24699322.2017.1379242
• Languages: English
• ISSNs: 2469-9322

Abstract

Abstract: Nowadays, sparse representation has been widely used in Magnetic Resonance Imaging (MRI). The commonly used sparse representation methods are based on symmetrical partition, which have not considered the complex structure of MRI image. In this paper, we proposed a sparse representation method for the brain MRI image, called GNAMlet transform, which is based on the gradient information and the non-symmetry and anti-packing model. The proposed sparse representation method can reduce the lost detail information, improving the reconstruction accuracy. The experiment results show the superiority of the proposed transform for the brain MRI image representation in comparison with some state-of-the-art sparse representation methods.