Modified local entropy-based transition region extraction and thresholding

Zuoyong Li, Dapeng Zhang, Yong Xu, Chuancai Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

32 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)5630-5638
Number of pages9
JournalApplied Soft Computing Journal
Issue number8
Publication statusPublished - 1 Dec 2011


  • Human visual perception
  • Image segmentation
  • Local entropy
  • Thresholding
  • Transition region

ASJC Scopus subject areas

  • Software


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