Authors: Pei Yang; Wei Song; Xiaobing Zhao; Rui Zheng; Letu Qingge
Addresses: Department of Computer Technology and Application, Qinghai University, Xining 810016, China ' School of Information Engineering, Minzu University of China, Beijing 100081, China ' School of Information Engineering, Minzu University of China, Beijing 100081, China ' School of Information Engineering, Minzu University of China, Beijing 100081, China ' College of Computing and Informatics, University of North Carolina at Charlotte, NC 28223, USA
Abstract: 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.
Keywords: Otsu's algorithm; threshold segmentation; maximum interclass variance; single threshold segmentation.
International Journal of Computational Science and Engineering, 2020 Vol.22 No.1, pp.146 - 153
Received: 22 Mar 2019
Accepted: 10 Jun 2019
Published online: 04 May 2020 *