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Research Article | Open Access

MRI image enhancement based on feature clustering in the NSCT domain

Xia Chang( )Haixia ZhaoZhenxia Xue
Ningxia Key Laboratory of Intelligent Information and Big Data Processing, School of Math and Information Science, North Minzu University, Yinchuan, China
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Abstract

The noise and low clarity in magnetic resonance imaging (MRI) images impede the doctor's diagnosis. An MRI image enhancement method is proposed in the non-subsampled contourlet transform (NSCT) domain. The coefficients of the NSCT are classified as noise component, weak edges component and strong edges component by feature clustering. We modified the transform coefficients to enhance the MRI images. The coefficients corresponding to noise are set to zero, the coefficients corresponding to strong edges are essentially unchanged, and the coefficients corresponding to weak edges are enhanced by a simplified nonlinear gain function. It is shown that the proposed MRI image enhancement method has advantages in visual quality and objective evaluation indexes compared to the state-of-the-art methods.

CLC number: 91C20, 68U10

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AIMS Mathematics
Pages 15633-15658

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Cite this article:
Chang X, Zhao H, Xue Z. MRI image enhancement based on feature clustering in the NSCT domain. AIMS Mathematics, 2022, 7(8): 15633-15658. https://doi.org/10.3934/math.2022856

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Received: 29 December 2021
Revised: 24 May 2022
Accepted: 14 June 2022
Published: 15 August 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)