Study on the application of MRF and fuzzy clustering as well as the D-S theory to image segmentation of the human brain

Yihong Guan; Bin Guo; Rui Duan; Anding Mao

doi:10.1117/12.866668

19 August 2010 Study on the application of MRF and fuzzy clustering as well as the D-S theory to image segmentation of the human brain

Yihong Guan, Bin Guo, Rui Duan, Anding Mao

Author Affiliations +

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78201J (2010) https://doi.org/10.1117/12.866668
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

Abstract

A new image segmentation method based on Markov Random Field (MRF) and Two-Dimensional Histogram Method of Fuzzy Clustering as well as Dempster-Shafer (D-S) evidence theory is presented in this paper.The application of Markov Random Field to image restoration and segmentation can effectively remove noise and get more accurate segmentation results; And the application of Fuzzy Clustering Theory together with Two-Dimensional Histogram image segmentation methods can get more satisfactory segmentation results; However, these two ways leads to different classification results while classifying the controversial pixels in images, so we can use the Dempster-Shafer evidence theory to assign the controversial points to the plausibility interval, and then divide them. This paper will adopt the above three theories to propose a human brain image segmentation research method. Experimental result shows that the method solves the problem of the class attribution of the controversial points, and the segmentation result is more in line with human vision.

Citation Download Citation

Yihong Guan, Bin Guo, Rui Duan, and Anding Mao "Study on the application of MRF and fuzzy clustering as well as the D-S theory to image segmentation of the human brain", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201J (19 August 2010); https://doi.org/10.1117/12.866668

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