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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Veröffentlicht in:Journal of biomedical optics Jg. 22; H. 1; S. 16012
Hauptverfasser: Sun, Yankui, Li, Shan, Sun, Zhongyang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.01.2017
Schlagworte:
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
ISSN:1560-2281
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Zusammenfassung: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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ISSN:1560-2281
DOI:10.1117/1.JBO.22.1.016012