Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques

Open access


Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] Lee N Laine AF Smith RT. A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration. In Engineering in Medicine and Biology Society 2007. EMBS 2007. 29th Annual International Conference of the IEEE pp. 4965-4968. IEEE 2007.

  • [2] Sopharak A Dailey NM Uyyanonvara B et al. Machine learning approach to automatic exudate detection in retinal images from diabetic patients. Journal of Modern Optics. 2010;57(2):124-135.

  • [3] Giancardo L Meriaudeau F Karnowski TP et al. Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical Image Analysis. 2012;16(1):216-226.

  • [4] Geetha Ramani R Balasubramanian L Jacob SG. Automatic Prediction of Diabetic Retinopathy and Glaucoma through Retinal Image Analysis and Data Mining Techniques. 2012 International Conference on Machine Vision and Image Processing (MVIP) Taipei 2012 pp. 149-152. IEEE 201.

  • [5] Wagle S Mangai JA Kumar VS. An Improved Medical Image Classification Model using Data Mining Techniques. GCC Conference and exhibition November 17-20 Doha Qatar. IEEE 2013.

  • [6] Hatanaka Y Muramatsu C Sawada A et al. Glaucoma Risk Assessment Based on Clinical Data and Automated Nerve Fiber Layer Defects Detection. 34th Annual International Conference of the IEEE EMBS San Diego California USA 28 August - 1 September 2012.

  • [7] Osareh A Shadgar B Markha R. A Computational Intelligence Based Approach for Detection of Exudates in Diabetic Retinopathy Images. IEEE Transactions on Information Technology in Biomedicine 2009;13(4):535-545.

  • [8] Youssef D Solouma N El-dib A et al. New Feature-Based Detection of Blood Vessels and Exudates in Color Fundus Images. 2010 2nd International Conference Image Processing Theory Tools and Applications (IPTA) Paris. pp. 294-299. 2010.

  • [9] Zhang X Thibault G Decenciere E et al. A. Exudate Detection in Color Retinal Images for Mass Screening of Diabetic Retinopathy. Medical Image Analysis. 2014;18(7):1026-1043.

  • [10] Mansour RF Abdelrahim EM Al-Johani AS. Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine. Journal of Intelligent Learning Systems and Applications. 2013;5:135-142.

  • [11] Sreng S Takada J Maneerat N et al. Automatic exudate extraction for early detection of diabetic retinopathy. Proceedings of the International Conference on Information Technology and Electrical Engineering Oct. 7-8 IEEE Xplore Press Yogyakarta pp: 31-35 2013.

  • [12] Franklin SW Rajan SE. Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images. IET Image Processing. 2014;8(1):601-609.

  • [13] Roychowdhury S Koozekanani DD Parhi KK. DREAM: Diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Informat. 2014;18(5):1717-1728

  • [14] Liu Q Zou B Chen J et al. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images. Comput Med Imag Graph. 2017;55:78-86.

  • [15] Caramihale T Popescu D Ichim L. Interconnected neural networks based on voting scheme and localdetectors for retinal image analysis and diagnosis. In Proceedings of the Image Analysis and Processing Conference - ICIAP 2017 Catania Italy Battiato 11–15 September 2017; Gallo G Schettini R Stanco F (Eds). Lecture Notes in Computer Science. 2017;10485:753-764.

  • [16] Raja SS Vasuki S. Screening diabetic retinopathy in developing countries using retinal images. Appl Med Inform. 2015;36(1):13-22.

  • [17] Sathya J Geetha K. Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm. Pol J Med Phys Eng. 2017;23(2):29-36.

  • [18] Gibbs P Turnbull LW. Textural analysis of contrast enhanced MR images of the breast. Magn Reson Med. 2003;50(1):92-98.

  • [19] Agner SC Soman S Libfeld E et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging. 2011;24(3):446-463.

  • [20] Haralick RM Shanmugam K. Textural features for image classification. IEEE Transactions on Systems Man and Cybernetics. 1973;3(6):610-621.

  • [21] Vickers AJ. Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data. BMC Med Res Methodol. 2005;35(5):1-12.

  • [22] Haury AC Gestraud P Vert JP. The influence of feature selection methods on accuracy stability and interpretability of molecular signatures. PloS One. 2011;6(12):e28210.

  • [23] Foody GM. Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference equivalence and non-inferiority. Remote Sensing of Environment. 2009;113(8):1658-1663.

  • [24] Liu H Li J Wong L. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Inform. 2002;13:51-60.

  • [25] Lisboa PJ. A review of evidence of health benefits from artificial neural networks in medical intervention. Neural Networks. 2002;15(1):11-39.

  • [26] Levman J Leung T Causer P et al. Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Transaction on Medical Imaging. 2008;27(5):688-696.

  • [27] Mahendran G Dhanasekaran R Narmadha Devi KN. Identification of Exudates for Diabetic Retinopathy based on Morphological Process and PNN Classifier. International Conference on Communication and Signal Processing April 3-5 2014 India

Journal information
Impact Factor

CiteScore 2018: 0.38

ICV 2017 = 103.49

SCImago Journal Rank (SJR) 2018: 0.132
Source Normalized Impact per Paper (SNIP) 2018: 0.303

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 148 148 9
PDF Downloads 103 103 4