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3D MR image denoising using rough set and kernel PCA method.

Abstract：

:In this paper, we have presented a two stage method, using kernel principal component analysis (KPCA) and rough set theory (RST), for denoising volumetric MRI data. A rough set theory (RST) based clustering technique has been used for voxel based processing. The method groups similar voxels (3D cubes) using class and edge information derived from noisy input. Each clusters thus formed now represented via basis vector. These vectors now projected into kernel space and PCA is performed in the feature space. This work is motivated by idea that under Rician noise MRI data may be non-linear and kernel mapping will help to define linear separator between these clusters/basis vectors thus used for image denoising. We have further investigated various kernels for Rician noise for different noise levels. The best kernel is then selected on the performance basis over PSNR and structure similarity (SSIM) measures. The work has been compared with state-of-the-art methods under various measures for synthetic and real databases.

journal_name

Magn Reson Imaging

journal_title

Magnetic resonance imaging

authors

Phophalia A,Mitra SK

doi

10.1016/j.mri.2016.10.010

subject

Has Abstract

pub_date

2017-02-01 00:00:00

pages

135-145

eissn

0730-725X

issn

1873-5894

pii

S0730-725X(16)30157-6

journal_volume

36

pub_type

杂志文章

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