Modified local entropy-based transition region extraction and thresholding

Authors:
Zuoyong Li;David Zhang;Yong Xu;Chuancai Liu
Affiliations:
Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong and Department of Computer Science, Minjiang University, Fuzhou 350108, Chin ...;Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong and Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institut ...;Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Transition region-based thresholding is a newly developed image binarization technique. Transition region descriptor plays a key role in the process, which greatly affects accuracy of transition region extraction and subsequent thresholding. Local entropy (LE), a classic descriptor, considers only frequency of gray level changes, easily causing those non-transition regions with frequent yet slight gray level changes to be misclassified into transition regions. To eliminate the above limitation, a modified descriptor taking both frequency and degree of gray level changes into account is developed. In addition, in the light of human visual perception, a preprocessing step named image transformation is proposed to simplify original images and further enhance segmentation performance. The proposed algorithm was compared with LE, local fuzzy entropy-based method (LFE) and four other thresholding ones on a variety of images including some NDT images, and the experimental results show its superiority.