Supervised grayscale thresholding based on transition regions

Authors:
Qingmao Hu;Suhuai Luo;Yu Qiao;Guoyu Qian
Affiliations:
Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences, The Chinese University of Hong Kong, China and Key Laboratory for Biomedical Informatics and Health Engineering, ...;School of Design, Communication and Information Technology, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia;Biomedical Imaging Laboratory, Agency for Science, Technology and Research, 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore;School of Design, Communication and Information Technology, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia and Biomedical Imaging Laboratory, Agency for Science, Tec ...
Venue:
Image and Vision Computing
Year:
2008

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Abstract

A new thresholding framework is proposed which is transition region based, and consists of deriving the transition region with the help of supervision and calculating the threshold from the transition region. Four ways of supervision are studied: picking up an object and a background pixel, from other clustering or segmentation results, based on sample statistics, and exploration of background proportions. The approach has been validated both quantitatively and qualitatively. It is found that the proposed approach: (1) is more robust, consistent and reliable than the conventional transition-region-based thresholding methods; and (2) is easier to implement and has wider applicability than existing supervised thresholding methods. The approach is especially useful for segmenting difficult images with multiple objects and/or serious imaging artifacts.