期刊
INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING
卷 22, 期 1, 页码 146-153出版社
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJCSE.2020.107266
关键词
Otsu's algorithm; threshold segmentation; maximum interclass variance; single threshold segmentation
资金
- National Science Foundation Project of P. R. China [61503424, 61331013, 61866031, 61762074]
- China Scholarship Council
- First Class Discipline of Minzu University of China (Intelligent Computing and Network Security)
- Young and Middle-aged Scientific Research Foundation Project of Qinghai University [2016-QGY-5, 2017-QGY-4, 2018-QGY-6]
- First Class University
Image segmentation is widely used as a fundamental step for various image processing applications. This paper focuses on improving the famous image thresholding method named Otsu's algorithm. Based on the fact that threshold acquired by Otsu's algorithm tends to be closer to the class with larger intraclass variance when the foreground and background have large intraclass variance difference, an improved strategy is proposed to adjust the threshold bias. We analysed the relationship between pixel greyscale value and the change of cumulative pixel number, and selected the ratio of pixel grey level value to a certain cumulative pixel number as the adjusted threshold. Experiments using typical testing images were set up to verify the proposed method both quantitatively and qualitatively. Two widely used metrics named misclassification error (ME) and dice similarity coefficient (DSC) were adopted for quantitative evaluation, and both quantitative and qualitative results indicated that the proposed algorithm could better segment the testing images and get competitive misclassification error and DSC values compared with Otsu's method and its improved versions proposed by Hu and Gong (2009) and Xu et al. (2011), and the time consumption of our method can be significantly reduced.
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