Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99253
Title: An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation
Authors: Wang, Zhimin
Song, Qing
Soh, Yeng Chai
Sim, Kang
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2013
Source: Wang, Z., Song, Q., Soh, Y. C., & Sim, K. (2013). An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation. Computer Vision and Image Understanding, 117(10), 1412-1420.
Series/Report no.: Computer vision and image understanding
Abstract: This paper presents an adaptive spatial information-theoretic fuzzy clustering algorithm to improve the robustness of the conventional fuzzy c-means (FCM) clustering algorithms for image segmentation. This is achieved through the incorporation of information-theoretic framework into the FCM-type algorithms. By combining these two concepts and modifying the objective function of the FCM algorithm, we are able to solve the problems of sensitivity to noisy data and the lack of spatial information, and improve the image segmentation results. The experimental results have shown that this robust clustering algorithm is useful for MRI brain image segmentation and it yields better segmentation results when compared to the conventional FCM approach.
URI: https://hdl.handle.net/10356/99253
http://hdl.handle.net/10220/17364
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2013.05.001
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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