A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

The detection of exudates is a prerequisite for detecting and grading severe retinal lesions, like the diabetic macular edema. In this work, we present a new method based on mathematical morphology for detecting exudates in color eye fundus images. A preliminary evaluation of the proposed method performance on a known public database, namely DIARETDB1, indicates that it can achieve an average sensitivity of 70.48%, and an average specificity of 98.84%. Comparing to other recent automatic methods available in the literature, our proposed approach potentially can obtain better exudate detection results in terms of sensitivity and specificity.

[1]  Majid Mirmehdi,et al.  Classification and Localisation of Diabetic-Related Eye Disease , 2002, ECCV.

[2]  Bunyarit Uyyanonvara,et al.  Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering , 2009, Sensors.

[3]  Joe G. Hollyfield,et al.  Retinal Degenerative Diseases , 2012, Advances in Experimental Medicine and Biology.

[4]  Bunyarit Uyyanonvara,et al.  Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods , 2008, Comput. Medical Imaging Graph..

[5]  Frank Y. Shih,et al.  Image Processing and Mathematical Morphology: Fundamentals and Applications , 2017 .

[6]  B. Zinman,et al.  Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies. , 2003, Diabetes care.

[7]  The Eye in Clinical Practice , 1994 .

[8]  Majid Mirmehdi,et al.  Automatic Recognition of Exudative Maculopathy using Fuzzy C- Means Clustering and Neural Networks , 2001 .

[9]  Torsten Schlote....  Pocket Atlas of Ophthalmology , 2006 .

[10]  Majid Mirmehdi,et al.  Comparative Exudate Classification Using Support Vector Machines and Neural Networks , 2002, MICCAI.

[11]  Giri Babu Kande,et al.  Segmentation of Exudates and Optic Disk in Retinal Images , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[12]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[13]  Uğur Şevik,et al.  Automatic segmentation of age-related macular degeneration in retinal fundus images , 2008, Comput. Biol. Medicine.

[14]  Domenico Tegolo,et al.  Automated Detection of Optic Disc Location in Retinal Images , 2008, 2008 21st IEEE International Symposium on Computer-Based Medical Systems.

[15]  B. Thomas,et al.  Automated identification of diabetic retinal exudates in digital colour images , 2003, The British journal of ophthalmology.

[16]  I. Constable,et al.  Laser photocoagulation: ocular research and therapy in diabetic retinopathy. , 2006, Advances in experimental medicine and biology.

[17]  K.S. Park,et al.  Measurement of ocular torsion using digital fundus image , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[19]  Edward R. Dougherty,et al.  Hands-on Morphological Image Processing , 2003 .

[20]  W. Tasman,et al.  The Wills Eye Hospital Atlas of Clinical Ophthalmology , 2001 .

[21]  Joni-Kristian Kämäräinen,et al.  The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol , 2007, BMVC.

[22]  Langis Gagnon,et al.  RetsoftPlus: a tool for retinal image analysis , 2004 .

[23]  Jacob Scharcanski,et al.  Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach , 2010, Comput. Biol. Medicine.