影像科学与光化学 ›› 2022, Vol. 40 ›› Issue (2): 225-231.DOI: 10.7517/issn.1674-0475.211002

• 综述与论文 • 上一篇    下一篇

基于梯度场变换的MRI图像增强方法

吴梦飞1,2, 薛旭成1, 兰太吉1, 徐鑫伟1,2   

  1. 1. 中国科学院长春光学精密机械与物理研究所, 吉林 长春 130033;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2021-11-02 出版日期:2022-03-15 发布日期:2022-03-08
  • 通讯作者: 薛旭成
  • 基金资助:
    国家自然科学基金(62005280)

MRI Image Enhancement Method by Gradient Field Transform

WU Mengfei1,2, XUE Xucheng1, LAN Taiji1, XU Xinwei1,2   

  1. 1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, Jilin, P. R. China;
    2. University of Chinese Academy of Sciences, Beijing 100049, P. R. China
  • Received:2021-11-02 Online:2022-03-15 Published:2022-03-08

摘要: 针对于部分MRI轮廓不清晰、细节模糊、噪声过大等缺点,提出一种改进的伽马校正图像增强的方法。该方法首先将图像变换到梯度域获得图像的梯度场;然后根据阈值将图像分割成两个不同的梯度区间,在不同梯度范围内进行相应的伽马变换以建立目标梯度场;最后根据变换后的目标梯度场重建出增强的图像。在图像重建过程中,本文通过对泊松方程进行数值求解以获得增强后的图像。为解决传统算法运算量较大的问题,本文先对图像进行分块处理,再组合拼接。结果表明:本算法使原图像的信息熵、图像平均差均有较大的提升。以本文选取的512×512 MRI图片为例,本算法将原图的信息熵从5.50提升到了6.94,图像标准差由33.70提升到了67.44。实验结果证明本方法在不提升图像噪声的基础上,能够有效地提升MRI的细节和轮廓信息。

关键词: 磁共振成像, 图像增强, 伽马校正, 梯度场, 偏微分方程, 泊松方程

Abstract: An improved gamma correction image enhancement method is proposed to solve the shortcomings of unclear outline, fuzzy details and excessive noise of some MRI. Firstly, the image is transformed into gradient domain to obtain the gradient field of the image. Then, the image is divided into two different gradient intervals according to the threshold, and the corresponding gamma transform is carried out in different gradient ranges to establish the target gradient field. Finally, the enhanced image is reconstructed according to the transformed target gradient field. In the process of image reconstruction, the enhanced image is obtained by numerically solving the Poisson equation. In order to solve the problem of large amount of computation of traditional algorithms, this paper first divides the image into blocks, and then combines and stitches it. The results show that this algorithm can greatly improve the information entropy and image average difference of the original image. Taking a 512×512 MRI selected in this paper as an example, this algorithm improves the information entropy of the original image from 5.50 to 6.94, and the image average difference from 33.70 to 67.44. Experimental results show that this method can effectively improve the detail and contour information of MRI without improving the image noise.

Key words: magnetic resonance imaging (MRI), image enhancement, gamma correction, gradient field, partial differential equation (PDE), Poisson equation