Detection of diabetic retinopathy using computational model of human visual system

Background: Diabetic retinopathy is one of the main causes of blindness and the most important complication of diabetes. The accurate analysis of retinal images is important in diagnosing this disease. In this study, a powerful and accurate algorithm for diagnosis of diabetic retinopathy, inspired by the human visual system, is presented based on the rapid sensitivity of the human visual system to intensity, direction and color. Materials and Methods: In this study, DIARETDB1 database containing selected images for diagnosis of diabetic retinopathy has been used. The Matlab R2013a software is used in Windows 7 with 2.5 GHz processor and 4 GHz RAM memory to implement various algorithms for images saliency maps. Results: The proposed method and four models of the existing methods on the DIARETDB1 database were tested, the results of which were based on visual comparison of the results, drawing the ROC curve and calculating the AUC of the models, showed the optimal performance of the proposed algorithm in comparison with other algorithms. The AUC of the proposed method was 0.9012, which in comparison with other methods is the highest value, indicating the proper function of this method in determining the correctness of image saliencies. Conclusion: The positive results from the proposed algorithm, which are based on image processing techniques and inspired by the human visual system, suggest that using this method can help ophthalmologists to diagnose fast, accurate, and reliable diabetic retinopathy.

[1]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[2]  Xiaohui Zhang,et al.  Detection and classification of bright lesions in color fundus images , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[3]  Ketki S. Argade,et al.  Automatic detection of diabetic retinopathy using image processing and data mining techniques , 2015, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).

[4]  Hassan Ghassemian,et al.  Texture-Gradient-Based Contour Detection , 2006, EURASIP J. Adv. Signal Process..

[5]  Nooshin Hadavi,et al.  Using image processing methods for diagnosis diabetic retinopathy , 2014, 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA).

[6]  Sabine Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[8]  Nicolai Petkov,et al.  Suppression of contour perception by band-limited noise and its relation to nonclassical receptive field inhibition , 2003, Biological cybernetics.

[9]  Nahed H. Solouma,et al.  Accurate detection of blood vessels improves the detection of exudates in color fundus images , 2012, Comput. Methods Programs Biomed..

[10]  K. Wahid,et al.  Automated Diabetic Retinopathy Detection Using Bag of Words Approach , 2017 .

[11]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[13]  M. Larsen,et al.  Automated detection of fundus photographic red lesions in diabetic retinopathy. , 2003, Investigative ophthalmology & visual science.

[14]  R. C. Tripathi,et al.  Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques , 2010 .

[15]  M. Sharif.  Microscopic Feature Extraction Method , 2013 .

[16]  S. Saravanakumar,et al.  Automatic Detection of Exudates in Retinal Images , 2018 .

[17]  David Zhang,et al.  A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy , 2009, IEEE Transactions on Information Technology in Biomedicine.

[18]  H. Kälviäinen,et al.  DIARETDB 1 diabetic retinopathy database and evaluation protocol , 2007 .

[19]  K.M.M. Rao,et al.  Low cost medical image processing system for rural/semi urban healthcare , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[20]  P Gain,et al.  Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. , 2010, Diabetes & metabolism.

[21]  M. Saranya,et al.  Fundus Image Screening for Diabetic Retinopathy , 2016 .

[22]  Hongyu Li,et al.  SDSP: A novel saliency detection method by combining simple priors , 2013, 2013 IEEE International Conference on Image Processing.

[23]  Nicolai Petkov,et al.  Contour detection based on nonclassical receptive field inhibition , 2003, IEEE Trans. Image Process..

[24]  J. Boyce,et al.  Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening , 2004, Diabetic medicine : a journal of the British Diabetic Association.

[25]  R. Manjula Sri,et al.  Novel image processing techniques to detect lesions using lab view , 2011, 2011 Annual IEEE India Conference.

[26]  M. Raza,et al.  Brain Image Enhancement - A Survey , 2012 .