Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8983
Title: Modified local entropy-based transition region extraction and thresholding
Authors: Li, Z
Zhang, D 
Xu, Y
Liu, C
Keywords: Human visual perception
Image segmentation
Local entropy
Thresholding
Transition region
Issue Date: 2011
Publisher: Elsevier
Source: Applied soft computing, 2011, v. 11, no. 8, p. 5630-5638 How to cite?
Journal: Applied soft computing 
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.
URI: http://hdl.handle.net/10397/8983
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2011.04.001
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