An image segmentation fusion algorithm based on density peak clustering and Markov random field

被引：1

作者：

Feng Y. ^{[1
,2
,3
]}

Liu W. ^{[1
]}

Zhang X. ^{[3
,4
]}

Zhu X. ^{[1
]}

机构：

[1] College of Computer Science and Engineering, Changchun University of Technology, Jilin, Changchun

[2] Artificial Intelligence Research Institute, Changchun University of Technology, Jilin, Changchun

[3] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Jilin, Changchun

[4] College of Computer Science and Technology, Jilin University, Jilin, Changchun

来源：

Multimedia Tools and Applications | 2024年 / 83卷 / 37期

关键词：

Density peak clustering; Image segmentation; Markov random field; Medical image; Segmentation fusion;

D O I：

10.1007/s11042-024-19502-3

中图分类号：

学科分类号：

摘要：

Image segmentation is a crucial task in the field of computer vision. Markov random fields (MRF) based image segmentation method can effectively capture intricate relationships among pixels. However, MRF typically requires an initial labeling field, and the number of classifications needs to be manually selected. To tackle these issues, we propose a novel medical image segmentation algorithm based on density peak clustering (DPC) and Markov random fields. Firstly, we improve DPC to make it applicable to grayscale images, named GIDPC. In the GIDPC method, local gray density and gray bias are defined to enable the automatic determination of the number of classifications. Then, GIDPC and MRF are combined to achieve image segmentation. Furthermore, a segmentation fusion method is employed to enhance the accuracy of image segmentation. We conduct comparison experiments on the whole brain atlas image library. Our proposed algorithm achieves high average values in uniformity measure, accuracy, precision and sensitivity, respectively. Experimental results demonstrate that the proposed algorithm outperforms other image segmentation methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

引用

页码：85331 / 85355

页数：24

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