Automated Classification of Severity of Age-Related Macular Degeneration from Fundus Photographs

Kankanahalli, Srihari; Burlina, Philippe; Wolfson, Yulia; Freund, David; Bressler, Neil M.

doi:10.1167/iovs.12-10928

Cited by 39 publications

(37 citation statements)

References 25 publications

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“…39 At the time of AREDS, grading was performed by trained, certified technicians under the supervision of ophthalmologists, and this method continues to be used in present studies. More recently with the advent of digital photography, several image analysis technologies are being used to explore the possibility of automated delineation of the area of GA. 43–47 …”

Section: Morphological Measures Of Geographic Atrophymentioning

confidence: 99%

Clinical Endpoints for the Study of Geographic Atrophy Secondary to Age-Related Macular Degeneration

Sadda

Chakravarthy

Birch

et al. 2016

Retina

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Purpose To summarize the recent literature describing the application of modern technologies in the study of patients with geographic atrophy (GA) secondary to age-related macular degeneration (AMD). Methods Review of the literature describing the terms and definitions used to describe GA, imaging modalities used to capture and measure GA, and the tests of visual function and functional deficits that occur in patients with GA. Results In this paper we describe the evolution of the definitions used to describe GA. We compare imaging modalities used in the characterization of GA, report on the sensitivity and specificity of the techniques where data exist, and describe the correlations between these various modes of capturing the presence of GA. We review the functional tests that have been used in patients with GA, and critically examine their ability to detect and quantify visual deficits. Conclusion Ophthalmologists and retina specialists now have a wide range of assessments available for the functional and anatomic characterization of GA in patients with AMD. To date, studies have been limited by their unimodal approach and we recommend that future studies of GA use multimodal imaging. We also suggest strategies for the optimal functional testing of patients with GA.

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Section: Morphological Measures Of Geographic Atrophymentioning

confidence: 99%

Clinical Endpoints for the Study of Geographic Atrophy Secondary to Age-Related Macular Degeneration

Sadda

Chakravarthy

Birch

et al. 2016

Retina

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“…Fewer ARIA investigations have been devoted to the automated detection and classification of images of age-related macular degeneration [3,11,18,28,32], and there is a relative paucity of image analysis studies dedicated specifically to automated GA characterization.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

Feeny¹,

Tadarati²,

Freund³

et al. 2015

Computers in Biology and Medicine

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Background-Age-related macular degeneration (AMD), left untreated, is the leading cause of vision loss in people older than 55. Severe central vision loss occurs in the advanced stage of the disease, characterized by either the in growth of choroidal neovascularization (CNV), termed the "wet" form, or by geographic atrophy (GA) of the retinal pigment epithelium (RPE) involving the center of the macula, termed the "dry" form. Tracking the change in GA area over time is important since it allows for the characterization of the effectiveness of GA treatments. Tracking GA evolution can be achieved by physicians performing manual delineation of GA area on retinal fundus images. However, manual GA delineation is time-consuming and subject to inter-and intraobserver variability.Methods-We have developed a fully automated GA segmentation algorithm in color fundus images that uses a supervised machine learning approach employing a random forest classifier. This algorithm is developed and tested using a dataset of images from the NIH-sponsored Age Related Eye Disease Study (AREDS). GA segmentation output was compared against a manual delineation by a retina specialist.Results-Using 143 color fundus images from 55 different patient eyes, our algorithm achieved PPV of 0.82±0.19, and NPV of 0:95±0.07.Discussion-This is the first study, to our knowledge, applying machine learning methods to GA segmentation on color fundus images and using AREDS imagery for testing. These HHS Public Access

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“…Based on anatomical structure detection, features of each fundus structure are extracted to detect pathological changes. Extensive studies have been performed on computer-aided diagnosis systems for ocular diseases such as microaneurysms [24], glaucoma [25–27], macular degeneration [28, 29], and diabetic retinopathy [30, 31]. …”

Section: Introductionmentioning

confidence: 99%

An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis

2017

Journal of Healthcare Engineering

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Cataract is one of the leading causes of blindness in the world's population. A method to evaluate blurriness for cataract diagnosis in retinal images with vitreous opacity is proposed in this paper. Three types of features are extracted, which include pixel number of visible structures, mean contrast between vessels and background, and local standard deviation. To avoid the wrong detection of vitreous opacity as retinal structures, a morphological method is proposed to detect and remove such lesions from retinal visible structure segmentation. Based on the extracted features, a decision tree is trained to classify retinal images into five grades of blurriness. The proposed approach was tested using 1355 clinical retinal images, and the accuracies of two-class classification and five-grade grading compared with that of manual grading are 92.8% and 81.1%, respectively. The kappa value between automatic grading and manual grading is 0.74 in five-grade grading, in which both variance and P value are less than 0.001. Experimental results show that the grading difference between automatic grading and manual grading is all within 1 grade, which is much improvement compared with that of other available methods. The proposed grading method provides a universal measure of cataract severity and can facilitate the decision of cataract surgery.

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Automated Classification of Severity of Age-Related Macular Degeneration from Fundus Photographs

Abstract: Development of an automated analysis for classification of age-related macular degeneration from digitized fundus photographs has high sensitivity and specificity when compared with expert graders and may have a role in screening or monitoring.

Cited by 39 publications

References 25 publications

Clinical Endpoints for the Study of Geographic Atrophy Secondary to Age-Related Macular Degeneration

Clinical Endpoints for the Study of Geographic Atrophy Secondary to Age-Related Macular Degeneration

Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images

An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis

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