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Journal of Medical Imaging

Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification
Author(s): Zhihong Hu; Gerard G. Medioni; Matthias Hernandez; Srinivas R. Sadda
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Paper Abstract

Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal patches in fundus autofluorescene (FAF) images. The image features include region-wise intensity measures, gray-level co-occurrence matrix measures, and Gaussian filter banks. A k-nearest-neighbor pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. Sixteen randomly chosen FAF images were obtained from 16 subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by a certified image reading center grader. Eight-fold cross-validation is applied to evaluate the algorithm performance. The mean overlap ratio (OR), area correlation (Pearson’s r), accuracy (ACC), true positive rate (TPR), specificity (SPC), positive predictive value (PPV), and false discovery rate (FDR) between the algorithm- and manually defined GA regions are 0.72±0.03, 0.98±0.02, 0.94±0.00, 0.87±0.01, 0.96±0.01, 0.80±0.04, and 0.20±0.04, respectively.

Paper Details

Date Published: 12 January 2015
PDF: 7 pages
J. Med. Imag. 2(1) 014501 doi: 10.1117/1.JMI.2.1.014501
Published in: Journal of Medical Imaging Volume 2, Issue 1
Show Author Affiliations
Zhihong Hu, Doheny Eye Institute (United States)
Gerard G. Medioni, The Univ. of Southern California (United States)
Matthias Hernandez, The Univ. of Southern California (United States)
Srinivas R. Sadda, Doheny Eye Institute (United States)
The Univ. of Southern California (United States)

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