Modified local entropy-based transition region extraction and thresholding

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.

[1]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[2]  Yan Solihin,et al.  Integral Ratio: A New Class of Global Thresholding Techniques for Handwriting Images , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Korris Fu-Lai Chung,et al.  Robust fuzzy clustering-based image segmentation , 2009, Appl. Soft Comput..

[4]  Jian Yang,et al.  Unsupervised range-constrained thresholding , 2011, Pattern Recognit. Lett..

[5]  Bir Bhanu,et al.  Automatic Target Recognition: State of the Art Survey , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Yutaka Hata,et al.  Fuzzy segmentation of endorrhachis in magnetic resonance images and its fuzzy maximum intensity projection , 2009, Appl. Soft Comput..

[7]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[8]  Yu Qiao,et al.  Supervised grayscale thresholding based on transition regions , 2008, Image Vis. Comput..

[9]  Márcio Portes de Albuquerque,et al.  Image thresholding using Tsallis entropy , 2004, Pattern Recognit. Lett..

[10]  Tianxu Zhang,et al.  Local entropy-based transition region extraction and thresholding , 2003, Pattern Recognit. Lett..

[11]  Mehmet Sezgin,et al.  A new dichotomization technique to multilevel thresholding devoted to inspection applications , 2000, Pattern Recognit. Lett..

[12]  J. J. Gerbrands.  Segmentation of noisy images , 1988 .

[13]  Etienne Barnard,et al.  Related approaches to gradient-based thresholding , 1993, Pattern Recognit. Lett..

[14]  Qingmao Hu,et al.  On minimum variance thresholding , 2006, Pattern Recognit. Lett..

[15]  Min Wang,et al.  Seeking multi-thresholds directly from support vectors for image segmentation , 2005, Neurocomputing.

[16]  Korris Fu-Lai Chung,et al.  A novel image thresholding method based on Parzen window estimate , 2008, Pattern Recognit..

[17]  Prasanta K. Panigrahi,et al.  Multilevel thresholding for image segmentation through a fast statistical recursive algorithm , 2006, Pattern Recognit. Lett..

[18]  Jan J. Gerbrands,et al.  Transition region determination based thresholding , 1991, Pattern Recognit. Lett..

[19]  Yu Qiao,et al.  Thresholding based on variance and intensity contrast , 2007, Pattern Recognit..

[20]  Zhang Tian,et al.  IMAGE TRANSITION REGION EXTRACTION AND SEGMENTATION BASED ON LOCAL COMPLEXITY , 2005 .

[21]  Thierry Pun,et al.  A new method for grey-level picture thresholding using the entropy of the histogram , 1980 .

[22]  William A. Yasnoff,et al.  Error measures for scene segmentation , 1977, Pattern Recognit..

[23]  Torbjørn Sund,et al.  An algorithm for fast adaptive image binarization with applications in radiotherapy imaging , 2003, IEEE Transactions on Medical Imaging.

[24]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .