Abstract Neovascularization is a serious visual consequence disease arising from Proliferative Diabetic Retinopathy (PDR). The condition causes progressive retinal damage in persons suffering from Diabetes mellitus, and is characterized by busted growth of abnormal blood vessels from the normal vasculature, which hampers proper blood flow into the retina because of oxygen insufficiency in retinal capillaries. The present paper aims at detecting PDR neovascularization with the help of the Adaptive Histogram Equalization technique, which enhances the green plane of the fundus image, resulting in enrichment of the details presented in the fundus image. The neovascularization blood vessels and the normal blood vessels were both segmented from the equalized image, using the Fuzzy C-means clustering technique. Marking of the neovascularization region, was achieved with a function matrix box based on a compactness classifier, which applied morphological and threshold techniques on the segmented image. Subsequently, the Feed Forward Back-propagation Neural Network interacted with extracted features (e.g., number of segments, gradient variation, mean, variance, standard deviation, contrast, correlation, entropy, energy, homogeneity, cluster shade towards the neovascularization detection region), in an attempt to achieve accurate identification. The above method was tested on images from three online datasets, as well as two hospital eye clinics. The performance of the detection technique was evaluated on these five image sources, and found to show an overall accuracy of 94.5% for sensitivity of 95.4% and of specificity 49.3% respectively, thus reiterating that the method would play a vital role in the study and analysis of Diabetic Retinopathy.

References

[1] Abr moff M., Garvin M., and Sonka M., Retinal Imaging and Image Analysis, IEEE Reviews in Biomedical Engineering, vol. 3, pp. 169-208, 2010.

[2] Akila T. and Kavitha G., Detection and Classification of Hard Exudates in Human Retinal Fundus Images Using Clustering and Random Forest Methods, International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 2, pp. 24-29, 2014.

[3] Belhadi S. and Benblidia N., Automated Retinal Vessel Segmentation using Entropic Thresholding Based Spatial Correlation Histogram of Gray Level Images, The International Arab Journal of Information Technology, vol. 12, no. 5, pp. 441-447, 2015.

[4] Boyd K. and Janigian R., What is Diabetic Retinopathy, Eye Smart, Eye health information from the American Academy of Opthalmology, The Eye M.D Association, Prohibited by US and international copyright law, 2013.

[5] Cheriyan J., Menon H., and Narayanankutty K., 3D Reconstruction of Human Retina from Fundus Image-A Survey, International Journal of Modern Engineering Research, vol. 2, no. 5, pp. 3089-3092, 2012.

[6] Christ J. and Parvathi R., Fuzzy C-Means Algorithm for Medical Image Segmentation, in Proceedings of 3rd International Conference on Electronics Computer Technology, Kanyakumari, pp. 33-36, 2011.

[7] Christ J. and Parvathi R., Segmentation of Medical Image using Clustering and Watershed Algorithms, American Journal of Applied Sciences, vol. 8, no. 12, pp. 1349-1352, 2011.

[8] Devasia T., Jacob P., and Thomas T., Automatic Optic Disc Boundary Extraction from Color Fundus Images, International Journal of Advanced Computer Science and Applications, vol. 5, no. 7, pp. 117-124, 2014.

[9] DIARETDB0 database. Available online: http://www2.it.lut.fi/project/imageret/diaretdb0/, Last Visited, 2007.

[10] DIARETDB1 database. Available online: http://www2.it.lut.fi/project/imageret/diaretdb1/, Last Visited, 2007.

[11] Fleming A., Philip S., Goatman K., Olson J., and Sharp P., Automated Microaneurysm Detection using Local Contrast Normalization and Local Vessel Detection, IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1223- 1232, 2006.

[12] Funmilola A., Oke O., Adedeji T., Alade O., and Adewusi E., Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation, Journal of Information Engineering and Applications, vol. 2, no. 6, pp. 21-32, 2012.

[13] Gerstenblith A. and Rabinowitz M., The Wills Eye Manual, Office and Emergency Room Diagnosis and Treatment of Eye Disease, Williams and Wilkins, 2012.

[14] Goatman K., Fleming A., Philip S., Williams G., Olson J., and Sharp P., Detection of New Vessels on the Optic Disc Using Retinal Photographs, IEEE Transactions on Medical Imaging, vol. 30, no. 4, pp. 972-979, 2011.

[15] Hassan S., Bong D., and Premsenthil M., Detection of Neovascularization in Diabetic 1008 The International Arab Journal of Information Technology, Vol. 15, No. 6, November 2018 Retinopathy, Journal Digit Imaging, vol. 25, no. 3, pp. 437-444, 2011.

[16] Kande G., Savithri S., and Subbaiah P., Segmentation of Vessels in Fundus Images using Spatially Weighted Fuzzy C-Means Clustering Algorithm, International Journal of Computer Science and Network Security, vol. 7, no. 12, pp. 102-109, 2007.

[17] Manimala K. and Gokulakrishnan K., Detection of Microaneurysms in Color Fundus Images, International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 2, pp. 30- 37, 2014.

[18] MESSIDOR database. Available online: http://messidor.crihan.fr/, Last Visited, 2004.

[19] Nandy M., Mandal A., Banerjee S., and Ray K., A Prescription Survey in Diabetes Assessing Metformin use in a Tertiary Care Hospital in Eastern India, Journal of Pharmacology and Pharmacotherapeutics, vol. 3, no. 3, pp. 273- 275, 2012.

[20] Niemeijer M., Abr moff M., and Ginneken B., Information Fusion for Diabetic Retinopathy CAD in Digital Color Fundus Photographs, IEEE Transactions on Medical Imaging, vol. 28, no. 5, pp. 775-785, 2009.

[21] Niemeijer M., Ginneken B., Staal J., Suttorp M., and Abr moff M., Automatic Detection of Red Lesions in Digital Color Fundus Photographs, IEEE Transactions on Medical Imaging, vol. 24, no. 5, pp. 584-592, 2005.

[22] Niemeijer M., Ginneken B., Cree M., Mizutani A., Quellec G., S nchez C., Zhang B., Hornero R., Lamard M., Muramatsu C., Wu X., Cazuguel G., You J., Mayo A., Li Q., Hatanaka Y., Cochener B., Roux C., Karray F., Garc a M., Fujita H., and Abr moff M., Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs, IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 185-195, 2010.

[23] Osareh A., Mirmehdi M., Thomas B., and Markham R., Automated Identification of Diabetic Retinal Exudates in Digital Colour Images, British Journal of Ophthalmology, vol. 87, no. 10, pp. 1220-1223, 2003.

[24] Padmanaban K. and Jagadeeshkannan R., Localization of Optic Disc Using Fuzzy C Means Clustering, International Conference on Current Trends in Engineering and Technology, Coimbatore, pp. 571-573, 2013.

[25] Raju A., Dwarakish G., and Reddy V., A Comparative Analysis of Histogram Equalization based Techniques for Contrast Enhancement and Brightness Preserving, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 6, no. 5, pp. 353-366, 2013.

[26] Suma K. and Kavitha V., Histogram Equalization based methods for Fundus Images: A Survey, in Proceedings of National Conference on Computing and Communication, pp. 206-210, 2015.

[27] Thacker T., India has Largest Number of Diabetes Patients, Report, 2009.

[28] Thomas N., Mahesh T., and Shunmuganathan K., Detection and Classification of Exudates in Diabetic Retinopathy, International Journal of Advance Research in Computer Science and Management Studies, vol. 2, no. 9, pp. 296-305, 2014.

[29] Vimala A. and Mohideen K., An Efficient Approach for Detection of Exudates in Diabetic Retinopathy Images Using Clustering Algorithm, Journal of Computer Engineering, vol. 2, no. 5, pp. 43-48, 2012.

[30] Vislisel J. and Oetting T., Diabetic Retinopathy: from One Medical Student to Another , EyeRounds.org, 2010.

[31] Viswanath K. and McGavin M., Diabetic Retinopathy: Clinical Findings and Management- Review Article, Community Eye Health, vol. 16, no. 46, pp. 21-24, 2003.

[32] Walter T., Klein J., Massin P., and Erginay A., A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy-Detection of Exudates in Color Fundus Images of the Human Retina, IEEE Transactions on Medical Imaging, vol. 21, no. 10, pp. 1236-1243, 2002. Detection of Neovascularization in Proliferative Diabetic Retinopathy Fundus Images 1009 Suma Gandhimathi received B.E., degree in Computer Science and Engineering in 2009, SMEC, Chennai affiliated to Anna University: Chennai. She has received M.E., degree in Computer Science and Engineering in 2012, Muthayammal Engineering College, Rasipuram affiliated to Anna University: Chennai. She is the University Rank Holder in PG. She is currently working toward the Ph.D., degree in Computer Science and Engineering in Anna University: Chennai. Her area of research interest is Medical Image Processing. Kavitha Pillai received her B.E degree in Computer Science and Engineering in 1996 from MS University and M.E degree in Computer Science and Engineering in 2000 from Madurai Kamaraj University. She is the University Rank Holder in UG and Gold Medalist in PG. She received Ph.D., degree in Computer Science and Engineering from Anna University Chennai in 2009. Right from 1996 she is in the Department of Computer Science & Engineering under various designations. Presently she is working as Associate Professor in the Department of CSE at University College of Engineering Kanchipuram, Anna University. Currently, under her guidance ten Research Scholars are pursuing Ph.D., as full time and part time. Her research interests are Wireless networks, Mobile Computing, Network Security, Wireless Sensor Networks, Image Processing, and Cloud Computing. She has published many papers in Conference, National and International journal in areas such as Network security, Mobile Computing, wireless network security, and Cloud Computing. She is a life time member of ISTE.