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RMCA-LSA: A Method of Monkey Brain Extraction

Hongxia Deng1, Chunxiang Hu1, Zihao Zhou2, Jinxiu Guo1, Zhenxuan Zhang3, Haifang Li1,*
1 Department of Information and Computer, Taiyuan University of Technology, Jin Zhong, 030600, China
2 Department of Mathematics, Taiyuan University of Technology, Jin Zhong, 030600, China
3 Ruyi Health Company, Atlanta of Georgia, America
* Corresponding Author: Haifang Li. Email:

Intelligent Automation & Soft Computing 2021, 29(2), 387-402. https://doi.org/10.32604/iasc.2021.016989

Received 17 January 2021; Accepted 28 February 2021; Issue published 16 June 2021

Abstract

The traditional level set algorithm selects the position of the initial contour randomly and lacks the processing of edge information. Therefore, it cannot accurately extract the edge of the brain tissue. In order to solve this problem, this paper proposes a level set algorithm that fuses partition and Canny function. Firstly, the idea of partition is fused, and the initial contour position is selected by combining the morphological information of each region, so that the initial contour contains more brain tissue regions, and the efficiency of brain tissue extraction is improved. Secondly, the canny operator is fused in the energy functional, which improves the accuracy of edge detection of rhesus monkey brain tissue while retaining the advantage of the traditional level set algorithm in processing an uneven gray image. Experimental results show that the algorithm can accurately extract the brain tissue of rhesus monkeys with an accuracy of up to 86%.

Keywords

Monkey brain extraction; magnetic resonance (MR); level set

Cite This Article

H. Deng, C. Hu, Z. Zhou, J. Guo, Z. Zhang et al., "Rmca-lsa: a method of monkey brain extraction," Intelligent Automation & Soft Computing, vol. 29, no.2, pp. 387–402, 2021.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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