Multi-Level Thresholding using Fuzzy Clustering Algorithm in Local Entropy-based Transition Region 


Vol. 12,  No. 5, pp. 587-594, Oct.  2005
10.3745/KIPSTB.2005.12.5.587


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  Abstract

This paper proposes a multi-level thresholding method for image segmentation using fuzzy clustering algorithm in transition region. Most of threshold-based image segmentation methods determine thresholds based on the histogram distribution of a given image. Therefore, the methods have difficulty in determining thresholds for real-image, which has a complex and undistinguished distribution, and demand much computational time and memory size. To solve these problems, we determine thresholds for real-image using fuzzy clustering algorithm after extracting transition region consisting of essential and important components in image. Transition region is extracted based on local entropy, which is robust to noise and is well-known as a tool that describes image information. And fuzzy clustering algorithm can determine optimal thresholds for real-image and be easily extended to multi-level thresholding. The experimental results demonstrate the effectiveness of the proposed method for performance.

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[IEEE Style]

J. T. Oh, B. R. Kim, W. H. Kim, "Multi-Level Thresholding using Fuzzy Clustering Algorithm in Local Entropy-based Transition Region," The KIPS Transactions:PartB , vol. 12, no. 5, pp. 587-594, 2005. DOI: 10.3745/KIPSTB.2005.12.5.587.

[ACM Style]

Jun Taek Oh, Bo Ram Kim, and Wook Hyun Kim. 2005. Multi-Level Thresholding using Fuzzy Clustering Algorithm in Local Entropy-based Transition Region. The KIPS Transactions:PartB , 12, 5, (2005), 587-594. DOI: 10.3745/KIPSTB.2005.12.5.587.

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