Automated segmentation of intraretinal cystoid macular edema based on Gaussian mixture model

被引:5
|
作者
Wu, Jinghong [1 ,2 ]
Niu, Sijie [3 ]
Chen, Qiang [4 ]
Fan, Wen [5 ]
Yuan, Songtao [5 ]
Li, Dengwang [1 ,2 ]
机构
[1] Shandong Normal Univ, Sch Phys & Elect, Shandong Key Lab Med Phys & Image Proc, Jinan 250358, Peoples R China
[2] Shandong Normal Univ, Sch Phys & Elect, Shandong Prov Engn & Tech Ctr Light Manipulat, Jinan 250358, Peoples R China
[3] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Peoples R China
[4] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
[5] Nanjing Med Univ, Dept Ophthalmol, Affiliated Hosp 1, Nanjing 210094, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Gaussian mixture model; level-set; spectral domain optical coherence tomography (SD-OCT); segmentation; OPTICAL COHERENCE TOMOGRAPHY; SUBRETINAL FLUID; IMAGES; QUANTIFICATION; REGIONS;
D O I
10.1142/S1793545819500202
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.
引用
收藏
页数:13
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