Improved fuzzy c-means segmentation algorithm for images with intensity inhomogeneity

Qingjie Zhao*, Jingjing Song, Yueyin Wu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节章节同行评审

4 引用 (Scopus)

摘要

Image segmentation is a classic problem in computer image comprehension and related fields. Up to now, there are not any general and valid partition methods which could satisfy different purposes, especially for medical images such as magnetic resonance images, which often corrupted by multiple imaging artifacts, for example intensity inhomogeneity, noise and partial volume effects. In this paper, we propose an improved fuzzy c-means image segmentation algorithm with more accurate results and faster computation. Considering two voxels with the same intensity belonging to the same tissue, we use q intensity levels instead of n intensity values in the objective function of the fuzzy c-means algorithm, which makes the algorithm clusters much faster since q is much smaller than n. Furthermore, a gain field is incorporate in the objective function to compensate for the inhomogeneity. In addition, we use c-means clustering algorithm to initialize the centroids. This can further accelerate the clustering. The test results show that the proposed algorithm not only gives more accurate results but also makes the computation faster.

源语言英语
主期刊名Analysis and Design of Intelligent Systems using Soft Computing Techniques
编辑Patricia Melin, Eduardo Gomez Ramirez, Janusz Kacprzyk, Witold Pedrycz
150-159
页数10
DOI
出版状态已出版 - 2007

出版系列

姓名Advances in Soft Computing
41
ISSN(印刷版)1615-3871
ISSN(电子版)1860-0794

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引用此

Zhao, Q., Song, J., & Wu, Y. (2007). Improved fuzzy c-means segmentation algorithm for images with intensity inhomogeneity. 在 P. Melin, E. Gomez Ramirez, J. Kacprzyk, & W. Pedrycz (编辑), Analysis and Design of Intelligent Systems using Soft Computing Techniques (页码 150-159). (Advances in Soft Computing; 卷 41). https://doi.org/10.1007/978-3-540-72432-2_16