AUTOMATIC DETECTION OF RETINAL MICROANEURYSMS USING HYBRID KERNEL SVM CLASSIFIER

Main Article Content

K. SINDHU
S. A. PRAYLIN SELVA BLESSY

Abstract

Retinal microaneurysms(MA) are the earliest clinical sign of diabetic retinopathy (DR) disease. DR is diagnosed by inspecting fundus images. Early detection of DR protects patients from losing their vision. In this paper an automatic method for detection of MA is proposed. This system utilizes preprocessing of retinal image to correct for non- uniform illumination and enhance contrast in the area of interest. The detection of MAs using Local Convergence index Features (LCF) and Hybrid kernel Support Vector Machine (HKSVM) classifier tells the probability of being actual MAs. The intensity based features and shape based features are extracted and combined using ensemble classifier positives will be reduced during the classification phase. The MA candidates are extracted and classified using HKSVM Classifier. The proposed method has been evaluated by public databases: Retinopathy Online Challenge (ROC) and e-optha. The efficiency and effectiveness of the method processed has been demonstrated by experimental results and thus proving its potential as a diagnostic tool for DR.

Keywords:
Diabetic retinopathy, microaneurysms, support vector machines, fundus images.

Article Details

How to Cite
SINDHU, K., & BLESSY, S. A. P. S. (2020). AUTOMATIC DETECTION OF RETINAL MICROANEURYSMS USING HYBRID KERNEL SVM CLASSIFIER. BIONATURE, 40(1), 17-28. Retrieved from https://globalpresshub.com/index.php/BN/article/view/835
Section
Original Research Article

References

Lee R, Wong TY, Sabanayagam C. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye and Vision. 2015; 2(1):17.

Spencer T, Olson JA, Hardy MC, Sharp PF, Forrester JV. An image processing strategy for the segmentation and quantification of micro-aneurysms in fluorescein angiograms of the ocular fundus comp. Biomed Res. 1996;29:284-302.

Hipwell JH, Strachan F, Olson JA, MCHardy KC, Sharp PF, Forrester JV. Automated detection of micro aneurysms in digital red-free photographs: A diabetic retinopathy screening tool. Diabetic Medicine. 2000;17:588–594.

Usher D, Dumskyj M, Himaga M. Automated detection of diabetic retinopathy in digital retinal images: A tool for diabetic retinopathy screening. Diabetic Medicine. 2002;21:84–90.

Niemeijer M, Van Ginneken B, Staal J, Suttorp-Schulten MSA, Abr`amoff MD. Automatic detection of red lesions in digital color fundus photographs. IEEE Transactions on Medical Imaging. 2005;24(5):584–592

Seiffert C, Khoshgoftaar TM, Hulse JV, Napolitano A. RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Trans. Syst., Man, Cybern., Syst. 2010;40(1):185–197.

Maher RS, Kayte SN, Meldhe ST, Dhopeshwarkar M. Automated diagnosis non-proliferative diabetic retinopathy in fundus images using support vector machine. International Journal of Computer Applications. 2015;125(15):7–10.

Maher R, Kayte S, Panchal D, Sathe P, Meldhe S. A decision support system for automatic screening of non-proliferative diabetic retinopathy. International Journal of Emerging Research in Management and Technology. 2015;4(10):18–24.

Zhang B, Karray F, Li Q, Zhang L. Sparse representation classifier for micro-aneurysm detection and retinal blood vessel extraction. Information Sciences. 2012;200:78–90.

Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Tobin KW, Chaum E. Microaneurysm detection with radon transform-based classification on retina images. In International Conference of the IEEE Engineering in Medicine and Biology Society. 2011;5939–5942.

Niemeijer M, Van Ginneken B, Cree MJ, et al. Retinopathy online challenge: Automatic detection of micro aneurysms in digital color fundus photographs. IEEE Trans. Med. Imag. 2010;29(1):185–195.

Decenciere E, Cazuguel G, Zhang X, et al. Tele Ophta: Machine learning and image processing methods for teleophthalmology. IRBM. 2013;34(2): 196–203.

Walter T, Massin P, Erginay A, et al. Automatic detection of micro-aneurysms in color fundus images. Med. Image Anal. 2007;11(6):555–566.

Su Wang, Hongying Lilian Tang, Lutfiah Ismail Al turk, Yin Hu, Saeid Sanei, George Michael Saleh, Tunde Peto. Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE Trans. Biomed. Eng. 2017;64(5):990–1002.

Yuji Hatanaka, Mitsuhiro Miyashita, Chisako Muramatsu. Automatic Micro-aneurysms Detection on Retinal Images Using Deep Convolution Neural Network. IEEE; 2018.

Kumar S, Kumar B. Diabetic retinopathy detection by extracting area and number of microaneurysm from colour fundus image. 5th International Conference on Signal Processing and Integrated Networks (SPIN); 2018.

Jiawei Xu, Xiaoqin Zhang, Huiling Chen, Jing Li, Jin Zhang, Ling Shao, Gang Wang. Automatic analysis of micro-aneurysms turnover to diagnose the progression of diabetic retino-pathy, IEEE. 2018;6.

Ling Dai, Ruogu Fang, Huating Li, Xuhong Hou, Bin Sheng, Qiang Wu, Weiping Jia. Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Transactions on Medical Imaging. 2018;37(5).