Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning
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-th...
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| Vydáno v: | Journal of biomedical optics Ročník 22; číslo 1; s. 16012 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
01.01.2017
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| ISSN: | 1560-2281 |
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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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| AbstractList | 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. |
| Author | Li, Shan Sun, Zhongyang Sun, Yankui |
| Author_xml | – sequence: 1 givenname: Yankui surname: Sun fullname: Sun, Yankui organization: Tsinghua University, Department of Computer Science and Technology, 30 Shuangqing Road, Haidian District, Beijing 100084, China – sequence: 2 givenname: Shan surname: Li fullname: Li, Shan organization: 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 – sequence: 3 givenname: Zhongyang surname: Sun fullname: Sun, Zhongyang organization: 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 |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28114453$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Diabetic Retinopathy - complications Diabetic Retinopathy - diagnostic imaging Humans Macular Degeneration - diagnostic imaging Macular Degeneration - etiology Macular Edema - diagnostic imaging Macular Edema - etiology Retina - diagnostic imaging Tomography, Optical Coherence - methods |
| Title | Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning |
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