Detection of Exudates from Fundus Images

With the rapid urbanization and growth of work-oriented atmosphere, stress in one’s life is not left behind. Though stress is casually linked with mental depression and exhaustion, it also induces physical malady. Diabetes is one such ailment that is caused by stress. If the blood sugar levels are not controlled, this may result in Diabetic Retinopathy, where the blood vessels break down in the retina of the eye, leading in formation of exudates. These irregularities caused due to accumulation of leaked lipids and fats, can be witnessed in fundus images. A novel algorithm is proposed in this paper to detect exudates. The technique has been developed on MATLAB. It involves elementary concepts of image processing to detect exudates, also to eliminate optic disc. This has been tested on 60 images. This method yields an accuracy of 90%.

[1]  Xiao Zhao,et al.  The connected-component labeling problem: A review of state-of-the-art algorithms , 2017, Pattern Recognit..

[2]  Mohammed Shafeeq Ahmed,et al.  Detection of exudates from RGB fundus images using 3σ control method , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[3]  H. Hassanpour,et al.  Using morphological transforms to enhance the contrast of medical images , 2015 .

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

[5]  Sudhir Rao Rupanagudi,et al.  A novel video processing based cost effective smart trolley system for supermarkets using FPGA , 2015, 2015 International Conference on Communication, Information & Computing Technology (ICCICT).

[6]  Sudhir Rao Rupanagudi,et al.  A novel and secure methodology for keyless ignition and controlling an automobile using air gestures , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[7]  Guy Cazuguel,et al.  FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE , 2014 .

[8]  Sudhir Rao Rupanagudi,et al.  A novel video processing based smart helmet for rear vehicle intimation & collision avoidance , 2015, 2015 International Conference on Computing and Network Communications (CoCoNet).

[9]  S. Mukhopadhyaya,et al.  Cache oblivious algorithm of average filtering in image processing , 2012, 2012 International Conference on Informatics, Electronics & Vision (ICIEV).

[10]  Handayani Tjandrasa,et al.  Exudate detection in retinal fundus images using combination of mathematical morphology and Renyi entropy thresholding , 2017, 2017 11th International Conference on Information & Communication Technology and System (ICTS).

[11]  Pushpa B. Patil,et al.  Detection of exudates for diabetic retinopathy using wavelet transform , 2017, 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI).

[12]  Igi Ardiyanto,et al.  Automated segmentation of hard exudates based on matched filtering , 2016, 2016 International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM).

[13]  Fabrice Mériaudeau,et al.  Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research , 2018, Data.

[14]  Sudhir Rao Rupanagudi,et al.  Novel methodology for Kannada Braille to speech translation using image processing on FPGA , 2014, 2014 International Conference on Advances in Electrical Engineering (ICAEE).

[15]  G. Muftuoglu,et al.  Pars Plana Vitrectomy in Advanced Coats’ Disease , 2011, Case Reports in Ophthalmology.

[16]  Sudhir Rao Rupanagudi,et al.  A simplified approach to assist motor neuron disease patients to communicate through video oculography , 2018, 2018 International Conference on Communication information and Computing Technology (ICCICT).

[17]  Priyadarshini Patil,et al.  An efficient method of detecting exudates in diabetic retinopathy: Using texture edge features , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[18]  Sudhir Rao Rupanagudi,et al.  A novel automatic low cost cutting machine-cum-3D printer using an image processing based control , 2015, 2015 IEEE Bombay Section Symposium (IBSS).

[19]  Jayant V. Kulkarni,et al.  Intensity features based classification of hard exudates in retinal images , 2015, 2015 Annual IEEE India Conference (INDICON).

[20]  M. Feinglos,et al.  Stress and Diabetes Mellitus , 1992, Diabetes Care.

[21]  Pranjali Kokare.  Wavelet based automatic exudates detection in diabetic retinopathy , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[22]  T. Ruba,et al.  Identification and segmentation of exudates using SVM classifier , 2015, 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).

[23]  R. Borjas,et al.  Developing a human prosthesis using a 3D printer in Honduras , 2015, 2015 IEEE Thirty Fifth Central American and Panama Convention (CONCAPAN XXXV).

[24]  Di Xiao,et al.  Exudate detection for diabetic retinopathy with convolutional neural networks , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).