Ji Eun Oh1, Hee Kyung Yang2, Kwang Gi Kim1, Jeong-Min Hwang2. 1. Biomedical Engineering Branch Division of Convergence Technology, National Cancer Center, Goyang, Korea. 2. Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Abstract
PURPOSE: To evaluate the validity of an automatic computer-aided diagnosis (CAD) system for detection of retinal nerve fiber layer (RNFL) defects on fundus photographs of glaucomatous and nonglaucomatous optic neuropathy. METHODS: We have proposed an automatic detection method for RNFL defects on fundus photographs in various cases of glaucomatous and nonglaucomatous optic neuropathy. In order to detect the vertical dark bands as candidate RNFL defects, the nonuniform illumination of the fundus image was corrected, the blood vessels were removed, and the images were converted to polar coordinates with the center of the optic disc. False positives (FPs) were reduced by using knowledge-based rules. The sensitivity and FP rates for all images were calculated. RESULTS: We tested 98 fundus photographs with 140 RNFL defects and 100 fundus photographs of healthy normal subjects. The proposed method achieved a sensitivity of 90% and a 0.67 FP rate per image and worked well with RNFL defects with variable depths and widths, with uniformly high detection rates regardless of the angular widths of the RNFL defects. The average detection accuracy was approximately 0.94. The overall diagnostic accuracy of the proposed algorithm for detecting RNFL defects among 98 patients and 100 healthy individuals was 86% sensitivity and 75% specificity. CONCLUSIONS: The proposed CAD system successfully detected RNFL defects in optic neuropathies. Thus, the proposed algorithm is useful for the detection of RNFL defects.
PURPOSE: To evaluate the validity of an automatic computer-aided diagnosis (CAD) system for detection of retinal nerve fiber layer (RNFL) defects on fundus photographs of glaucomatous and nonglaucomatous optic neuropathy. METHODS: We have proposed an automatic detection method for RNFL defects on fundus photographs in various cases of glaucomatous and nonglaucomatous optic neuropathy. In order to detect the vertical dark bands as candidate RNFL defects, the nonuniform illumination of the fundus image was corrected, the blood vessels were removed, and the images were converted to polar coordinates with the center of the optic disc. False positives (FPs) were reduced by using knowledge-based rules. The sensitivity and FP rates for all images were calculated. RESULTS: We tested 98 fundus photographs with 140 RNFL defects and 100 fundus photographs of healthy normal subjects. The proposed method achieved a sensitivity of 90% and a 0.67 FP rate per image and worked well with RNFL defects with variable depths and widths, with uniformly high detection rates regardless of the angular widths of the RNFL defects. The average detection accuracy was approximately 0.94. The overall diagnostic accuracy of the proposed algorithm for detecting RNFL defects among 98 patients and 100 healthy individuals was 86% sensitivity and 75% specificity. CONCLUSIONS: The proposed CAD system successfully detected RNFL defects in optic neuropathies. Thus, the proposed algorithm is useful for the detection of RNFL defects.