Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images

Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out.

[1]  Paria Mehrani,et al.  Superpixels and Supervoxels in an Energy Optimization Framework , 2010, ECCV.

[2]  Yugen Yi,et al.  Automatic Detection of Exudates in Digital Color Fundus Images Using Superpixel Multi-Feature Classification , 2017, IEEE Access.

[3]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Huiqi Li,et al.  Automated feature extraction in color retinal images by a model based approach , 2004, IEEE Transactions on Biomedical Engineering.

[6]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Feroui Amel,et al.  Improvement of the Hard Exudates Detection Method Used For Computer- Aided Diagnosis of Diabetic Retinopathy , 2012 .

[8]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  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.

[10]  David Cárdenas-Peña,et al.  Waterpixels , 2015, IEEE Transactions on Image Processing.

[11]  Vaïa Machairas,et al.  Waterpixels and their application to image segmentation learning. (Waterpixels et leur application à l'apprentissage statistique de la segmentation) , 2016 .

[12]  Desire Sidibé,et al.  Discrimination of retinal images containing bright lesions using sparse coded features and SVM , 2015, Comput. Biol. Medicine.

[13]  Gwénolé Quellec,et al.  Exudate detection in color retinal images for mass screening of diabetic retinopathy , 2014, Medical Image Anal..

[14]  Jacob Scharcanski,et al.  A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images , 2010, Comput. Medical Imaging Graph..

[15]  Kenneth W. Tobin,et al.  Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  Zhenhua Guo,et al.  Rotation invariant texture classification using LBP variance (LBPV) with global matching , 2010, Pattern Recognit..

[18]  C. Sinthanayothin,et al.  Automated detection of diabetic retinopathy on digital fundus images , 2002, Diabetic medicine : a journal of the British Diabetic Association.

[19]  Muhammad Younus Javed,et al.  Automated detection of exudates and macula for grading of diabetic macular edema , 2014, Comput. Methods Programs Biomed..

[20]  Shehzad Khalid,et al.  Detection and classification of retinal lesions for grading of diabetic retinopathy , 2014, Comput. Biol. Medicine.

[21]  Mariano Alcañiz Raya,et al.  Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology , 2013, IEEE Transactions on Medical Imaging.

[22]  Sharib Ali,et al.  Statistical atlas based exudate segmentation , 2013, Comput. Medical Imaging Graph..

[23]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Guy Cazuguel,et al.  TeleOphta: Machine learning and image processing methods for teleophthalmology , 2013 .