Local entropy-based transition region extraction and thresholding

Authors:
Chengxin Yan;Nong Sang;Tianxu Zhang
Affiliations:
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, PR China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, PR China;Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan 430074, PR China
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

Transition region based thresholding is a newly developed approach for image segmentation in recent years. Gradient-based transition region extraction methods (G-TREM) are greatly affected by noise. Local entropy in information theory represents the variance of local region and catches the natural properties of transition regions. In this paper, we present a novel local entropy-based transition region extraction method (LE-TREM), which effectively reduces the affects of noise. Experimental results demonstrate that LE-TREM significantly outperforms the conventional G-TREM.