Research Papers: Imaging

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

[+] Author Affiliations
Yankui Sun

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

Shan Li

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

Beihang University, School of Software, 37 Xueyuan Road, Haidian District, Beijing 100191, China

Zhongyang Sun

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

Sun Yat-Sen University, School of Data and Computer Science, 132 East Waihuan Road, Guangzhou Higher Education Mega Center (University Town), Guangzhou 510006, China

J. Biomed. Opt. 22(1), 016012 (Jan 20, 2017). doi:10.1117/1.JBO.22.1.016012
History: Received October 3, 2016; Accepted December 27, 2016
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Abstract.  We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects—15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing—168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Yankui Sun ; Shan Li and Zhongyang Sun
"Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning", J. Biomed. Opt. 22(1), 016012 (Jan 20, 2017). ; http://dx.doi.org/10.1117/1.JBO.22.1.016012


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