Microaneurysms Detection in Fundus Image Based on Phase Congruency
1 School of Electronics and Information Engineering,Tianjin Polytechnic University,Tianjin, 300387, China
2 Tianjin Medical University Eye Hospital, Tianjin, 300384, China
3 Tianjin Medical University Metabolic Disease Hospital, Tianjin, 300070, China
Abstract:The presence of microaneurysms in the retina is the earliest clinical symptom of diabetic retinopathy(DR), thus their reliable detection is essential in the DR screening system. Based on phase congruency, this paper proposes a new microaneurysms detection method. The first step aimed at obtaining microaneurysms candidate regions achieved by using phase congruency. Then the irrelevant information, such as the vessel fragments, was removed by constructing directional crosssection profiles. Through testing on 50 fundus images provided by ROC website, the method achieved a sensitivity of 94%, specificity of 100%, and accuracy of 96% at the image level, respectively. This method can accurately get microaneurysms in color fundus images, and it is insensitive to image brightness and contrast.
[1]Spencer T, Olson JA, McHardy KC, et al. An imageprocessing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus [J]. Computers and Biomedical Research, 1996, 29(4): 284-302.
[2]Hipwell JH, Strachant F, Olson JA, et al. Automated detection of microaneurysms in digital redfree photographs: A diabetic retinopathy screening tool [J]. Diabetic Medicine, 2000, 17(8): 588-594.
[3]Fleming AD, Philip S, Goatman KA, et al. Automated microaneurysm detection using local contrast normalization and local vessel detection [J]. IEEE Transactions on Medical Imaging, 2006, 25(9):1223-1232.
[4]Walter T, Massin P, Erginay A, et al. Automatic detection of microaneurysms in color fundus images [J]. Medical Image Analysis, 2007, 11(6): 555-566.
[5]Sinthanayothin C, Boyce JF, Williamson TH, et al. Automated detection of diabetic retinopathy on digital fundus images [J]. Diabetic Medicine, 2002, 19(2):105-112.
[6]Niemeijer M, Ginneken B, Staal J, et al. Automatic detection of red lesions in digital color fundus photographs [J]. IEEE Transactions on Medical Imaging, 2005, 24(5): 584-592.
[7]Zhang B, Karray F, Zhang L, et al. Microaneurysm(MA) detection via sparse representation classifier with MA and NonMA dictionary learning [C] //International Conference on Pattern Recognition. Istanbul: IEEE, 2010: 277-280.
[8]Quellec G, Lamard M, Josselin PM, et al. Optimal wavelet transform for the detection of microaneurysms in retina photographs [J]. IEEE Transactions on Medical Imaging, 2008, 27(9): 1230-1241.
[9]Hatanaka Y, Inoue T, Okumura S, et al. Automated microaneurysm detection method based on doublering filter and feature analysis in retinal fundus images [C] //Proceedings of 25th International Symposium on ComputerBased Medical Systems. Rome: IEEE, 2012: 1-4.
[10]Xiao Zhitao, Hou Zhengxin. Phase based feature detector consistent with human visual system characteristics [J]. Pattern Recognition Letters, 2004, 25(10): 1115-1121.
[11]Morrone MC, Owens RA. Feature detection from local energy [J]. Pattern Recognition Letters, 1987, 6(5): 303-313.
[12]Venkatesh S, Owens RA. An energy feature detection scheme [C] // International Conference on Image Processing. Singapore: IEEE, 1989: 553-557.
[13]Kovesi P, Image features from phase congruency [J]. Journal of Computer Vision Research, 1999, 1(3): 1-26.
[14]Lazar I, Hajdu A. Retinal microaneurysm detection through local rotating crosssection profile analysis [J]. IEEE Transactions on Medical Imaging, 2013, 32(2): 400-407.
[15]Retinopathy Online Challenge [EB/OL]. http://roc.healthcare.uiowa. edu/, 2000-01-01/2013-08030.
[16]高玮玮, 沈建新, 王玉亮. 免散瞳眼底图像中微动脉瘤的高效自动检测 [J]. 中国生物医学工程学报, 2012, 31(6): 839-845.