Automatic Optic Disc Segmentation Based on Modified Local Image Fitting Model with Shape Prior Information.

Authors:
Yuan Gao
Yuan Gao
Zhejiang University
Hangzhou Shi | China
Chengdong Wu
Chengdong Wu
Northeastern University
Wei Zhou
Wei Zhou
University of Michigan
Ann Arbor | United States

J Healthc Eng 2019 14;2019:2745183. Epub 2019 Mar 14.

College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China.

Accurate optic disc (OD) detection is an essential yet vital step for retinal disease diagnosis. In the paper, an approach for segmenting OD boundary without manpower named full-automatic double boundary extraction is designed. There are two main advantages in it. (1) Since the performances and the computational cost produced by iterations of contour evolution of active contour models- (ACM-) based approaches greatly depend on the initialization, this paper proposes an effective and adaptive initial level set contour extraction approach using saliency detection and threshold techniques. (2) In order to handle unreliable information generated by intensity in abnormal retinal images caused by diseases, a modified LIF approach is presented by incorporating the shape prior information into LIF. We test the effectiveness of the proposed approach on a publicly available DIARETDB0 database. Experimental results demonstrate that our approach outperforms well-known approaches in terms of the average overlapping ratio and accuracy rate.

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Source
http://dx.doi.org/10.1155/2019/2745183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437741PMC
March 2019
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