An Enhanced Spatial Fuzzy C-Means Algorithm for Image Segmentation (An Enhanced Spatial Fuzzy C-Means Algorithm for Image Segmentation )

Tung X. Truong (퉁투룽); 김종면

An Enhanced Spatial Fuzzy C-Means Algorithm for Image Segmentation

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2012, 17(2), pp.49-57
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Tung X. Truong ¹, 김종면 ¹

¹울산대학교

Accredited

ABSTRACT

Conventional fuzzy c-means (FCM) algorithms have achieved a good clustering performance. However, they do not fully utilize the spatial information in the image and this results in lower clustering performance for images that have low contrast, vague boundaries, and noises. To overcome this issue, we propose an enhanced spatial fuzzy c-means (ESFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors in a 3x3 square window. To evaluate between the proposed ESFCM and various FCM based segmentation algorithms, we utilized clustering validity functions such as partition coefficient (), partition entropy (), and Xie-Bdni function (). Experimental results show that the proposed ESFCM outperforms other FCM based algorithms in terms of clustering validity functions.

KEYWORDS

uzzy c-means algorithm, image segmentation, membership function, clustering validity functions

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65