Automated lesion detection in retinal images

This paper describes automated lesion detection in retinal images. Physicians and ophthalmologists assess retinal images for several kinds of lesions, including hemorrhages, exudates, and arteriolar narrowing. Hemorrhage is a major sign of diabetic retinopathy, which is the second most common cause of vision loss. Arteriolar narrowing is a major sign of hypertensive retinopathy. The aim of this study was to measure arteriolar-to-venular diameter ratio for the detection of arteriolar narrowing and to develop a hemorrhage detection method. Blood vessels and hemorrhages were extracted using a double-ring filter. This filter device calculates the difference between the average pixel values of the inside and outside regions. Arteriolar narrowing is determined based on major arteriolar-to-venular diameter ratios. Thus, the major blood vessels were extracted and the arteriolar-to-venular diameter ratio was automatically calculated based on the artery and vein diameter measurements. Finally, the hemorrhage candidates remained after the blood vessels were "erased" from the image and hemorrhages were detected by machine learning methods using 64 texture features. We tested 20 retinal images from the DRIVE database to evaluate our proposed arteriolar-to-venular diameter ratio measurement method. Both the average error and the standard deviation of the arteriolar-to-venular diameter ratio measurements were 0.07 ± 0.06. We evaluated the proposed method for hemorrhage detection by testing 71 retinal images, including 53 images with hemorrhages and 18 normal ones. The sensitivity and specificity for the detection of abnormal cases were 83% and 67%, respectively.

[1]  Hiroshi Fujita,et al.  Three-dimensional Reconstruction using a Single Two-dimensional Retinal Image , 2006 .

[2]  Hiroshi Fujita,et al.  Recognition of Optic Nerve Head Using Blood-Vessel-Erased Image and Its Application to Production of Simulated Stereogram in Computer-Aided Diagnosis System for Retinal Images , 2006 .

[3]  Hiroshi Fujita,et al.  Determination of cup-to-disc ratio of optical nerve head for diagnosis of glaucoma on stereo retinal fundus image pairs , 2009, Medical Imaging.

[4]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[5]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[6]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[7]  Masakazu IWAMURA,et al.  Character Recognition with Mahalanobis Distance Based on Between-Cluster Information , .

[8]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9]  Hiroshi Fujita,et al.  Automatic Measurement of Vertical Cup-to-Disc Ratio on Retinal Fundus Images , 2010, ICMB.

[10]  Hiroshi Fujita,et al.  CAD scheme to detect hemorrhages and exudates in ocular fundus images , 2007, SPIE Medical Imaging.

[11]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[12]  Worthie Doyle,et al.  Operations Useful for Similarity-Invariant Pattern Recognition , 1962, JACM.

[13]  Hiroshi Fujita,et al.  Automated detection and classification of major retinal vessels for determination of diameter ratio of arteries and veins , 2010, Medical Imaging.

[14]  H. Fujita,et al.  Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma. , 2010, Journal of biomedical optics.

[15]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Hiroshi Fujita,et al.  Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods , 2011, Comput. Methods Programs Biomed..

[17]  Hiroshi Fujita,et al.  Quantitative depth analysis of optic nerve head using stereo retinal fundus image pair. , 2008, Journal of biomedical optics.

[18]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[19]  Hiroshi Fujita,et al.  Improvement of automated detection method of hemorrhages in fundus images , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Xiangrong Zhou,et al.  Automated Detection Algorithm for Arteriolar Narrowing on Fundus Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.