Extraction of Exudates from the Fundus Images A Review

The dawn of the 20th century has heralded the entry of technology into almost every sphere of our daily lives, specially healthcare. This paper deals with review of the various methods used to detect exudates present in the eye which is the indicator of diabetic retinopathy, a dreadful eye disease. The aim here is to go through the algorithms using image processing technique giving importance to highest possible accuracy. The paper concludes with an insight into the future scope of this research and how an economically viable solution can be further

[1]  B. Ramasubramanian,et al.  An efficient system for the detection of exudates in colour fundus images using image processing technique , 2014, 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies.

[2]  A. Bin Mansoor,et al.  Enhancement of exudates for the diagnosis of diabetic retinopathy using Fuzzy Morphology , 2008, 2008 IEEE International Multitopic Conference.

[3]  Nahed H. Solouma,et al.  New feature-based detection of blood vessels and exudates in color fundus images , 2010, 2010 2nd International Conference on Image Processing Theory, Tools and Applications.

[4]  Oge Marques,et al.  Morphological Image Processing , 2011 .

[5]  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.

[6]  Alireza Osareh,et al.  A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images , 2009, IEEE Transactions on Information Technology in Biomedicine.

[7]  D. Kavitha,et al.  Automatic detection of optic disc and exudates in retinal images , 2005, Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, 2005..

[8]  Giri Babu Kande,et al.  Extraction of exudates and blood vessels in digital fundus images , 2008, 2008 8th IEEE International Conference on Computer and Information Technology.

[9]  Jun-ichi Takada,et al.  Automatic exudate extraction for early detection of Diabetic Retinopathy , 2013, 2013 International Conference on Information Technology and Electrical Engineering (ICITEE).

[10]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[11]  F. A. Wies.  Diseases of the Retina , 1946, The Yale Journal of Biology and Medicine.

[12]  Ravinda G. N. Meegama,et al.  Detection of hard exudates from diabetic retinopathy images using fuzzy logic , 2013, IET Image Process..

[13]  Sylvain Chartier,et al.  The k-means clustering technique: General considerations and implementation in Mathematica , 2013 .

[14]  Bhailal Limbasiya,et al.  A Review Paper On Detection And Extraction Of Blood Vessels, Microaneurysms And Exudates From Fundus Images , 2013 .

[15]  A. Aghagolzadeh,et al.  Better detection of retinal abnormalities by accurate detection of blood vessels in retina , 2014, 2014 22nd Iranian Conference on Electrical Engineering (ICEE).

[16]  V. Vijaya Kumari,et al.  Feature Extraction for Early Detection of Diabetic Retinopathy , 2010, 2010 International Conference on Recent Trends in Information, Telecommunication and Computing.

[17]  Siti Zaiton Mohd Hashim,et al.  Diabetic retinopathy lesion detection using region-based approach , 2014, 2014 8th. Malaysian Software Engineering Conference (MySEC).

[18]  R. Hornero,et al.  Automatic Image Processing Algorithm to Detect Hard Exudates based on Mixture Models , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  V. Vijayakumari,et al.  Detection of exudates and feature extraction of retinal images using fuzzy clustering method , 2013 .

[20]  Apurba Das,et al.  Image Enhancement in Spatial Domain , 2018, Understanding Digital Image Processing.

[21]  R. Hornero,et al.  Feature Extraction and Selection for the Automatic Detection of Hard Exudates in Retinal Images , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  R. Catherine Silvia,et al.  Detection of Non-Proliferative Diabetic Retinopathy in fundus images of the human retina , 2013, 2013 International Conference on Information Communication and Embedded Systems (ICICES).

[23]  Sven Loncaric,et al.  Weighted ensemble based automatic detection of exudates in fundus photographs , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Sven Loncaric,et al.  Voting based automatic exudate detection in color fundus photographs , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[25]  R. Dhanasekaran,et al.  Morphological process based segmentation for the detection of exudates from the retinal images of diabetic patients , 2014, 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies.

[26]  J. Nayak,et al.  Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

[27]  Hanung Adi Nugroho,et al.  Segmentation of exudates based on high pass filtering in retinal fundus images , 2015, 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE).

[28]  Adarsh Punnolil,et al.  A novel approach for diagnosis and severity grading of diabetic maculopathy , 2013, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[29]  R. Dhanasekaran,et al.  Identification of exudates for Diabetic Retinopathy based on morphological process and PNN classifier , 2014, 2014 International Conference on Communication and Signal Processing.

[30]  S. Dandapat,et al.  Detection of Diabetic Retinopathy in Fundus Images using Vector Quantization Technique , 2006, 2006 Annual IEEE India Conference.

[31]  Shuqian Luo,et al.  Support vector machine based method for identifying hard exudates in retinal images , 2009, 2009 IEEE Youth Conference on Information, Computing and Telecommunication.

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

[33]  C. Eswaran,et al.  Extraction of the Contours of Optic Disc and Exudates Based on Marker-Controlled Watershed Segmentation , 2008, 2008 International Conference on Computer Science and Information Technology.

[34]  Charbel Fares,et al.  Individuals Identification Using Tooth Structure , 2011, DICTAP.

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

[36]  Huchuan Lu,et al.  Automatic segmentation of hard exudates in fundus images based on boosted soft segmentation , 2010, 2010 International Conference on Intelligent Control and Information Processing.

[37]  C Kupfer,et al.  The International Agency for the Prevention of Blindness. , 1994, American journal of ophthalmology.

[38]  R. Dhanasekaran,et al.  Investigation of severity of diabetic retinopathy by detecting exudates with respect to macula , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).