Computer-Aided Diagnosis

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

[+] Author Affiliations
Zhihong Hu

University of California, Doheny Eye Institute, Los Angeles, California 90033, United States

Gerard G. Medioni, Matthias Hernandez

University of Southern California, Department of Computer Science, Los Angeles, California 90089, United States

Srinivas R. Sadda

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

J. Med. Imag. 2(1), 014501 (Jan 12, 2015). doi:10.1117/1.JMI.2.1.014501
History: Received March 31, 2014; Accepted December 11, 2014
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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.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Zhihong Hu ; Gerard G. Medioni ; Matthias Hernandez and Srinivas R. Sadda
"Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification", J. Med. Imag. 2(1), 014501 (Jan 12, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.1.014501


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