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J Med Imaging (Bellingham). 2015 Jan;2(1):014501. doi: 10.1117/1.JMI.2.1.014501. Epub 2015 Jan 12.

Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.

Author information

1
University of California , Doheny Eye Institute, Los Angeles, California 90033, United States.
2
University of Southern California , Department of Computer Science, Los Angeles, California 90089, United States.
3
University of California , Doheny Eye Institute, Los Angeles, California 90033, United States ; University of Southern California , Department of Ophthalmology, Los Angeles, California 90033, United States.

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 [Formula: see text]-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 [Formula: see text]), 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 [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively.

KEYWORDS:

fundus autofluorescene images; geographic atrophy; supervised classification

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