Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning-Reference-Cited by-同舟云学术

Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

Published:2017-01-20 Issue:1 Volume:22 Page:016012
ISSN:1083-3668
Container-title:Journal of Biomedical Optics
language:en
Short-container-title:J. Biomed. Opt

Author:

Sun Yankui¹,Li Shan²,Sun Zhongyang³

Affiliation:

1. Tsinghua University, Department of Computer Science and Technology, 30 Shuangqing Road, Haidian District, Beijing 100084, China

2. Tsinghua University, Department of Computer Science and Technology, 30 Shuangqing Road, Haidian District, Beijing 100084, ChinabBeihang University, School of Software, 37 Xueyuan Road, Haidian District, Beijing 100191, China

3. Tsinghua University, Department of Computer Science and Technology, 30 Shuangqing Road, Haidian District, Beijing 100084, ChinacSun Yat-Sen University, School of Data and Computer Science, 132 East Waihuan Road, Guangzhou Higher Education Mega Center (University Town), Guangzhou 510006, China

Publisher

SPIE-Intl Soc Optical Eng

Subject

Biomedical Engineering,Atomic and Molecular Physics, and Optics,Biomaterials,Electronic, Optical and Magnetic Materials

Reference42 articles.

2. A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes

3. Multiple-object geometric deformable model for segmentation of macular OCT

4. Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images

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