Total views : 167
A Methodical Approach for Segmentation of Diabetic Retinopathy Images
Background/Objective: Exudates are the significant portions for the detection of Diabetic Retinopathy (DR). This paper demonstrates a complete framework for the detection of hard exudates in retinopathy images. Methods/Statistical Analysis: This paper presents two variants of Multiple Kernel induced Gaussian Spatial Fuzzy-C Means (MKGSFCM) algorithm for the segmentation of retinal fundus images. The algorithm is applied on different DR images and the performance of the algorithm is evaluated qualitatively and quantitatively. Findings: FCM and KFCM algorithms are commonly used clustering methods but are very sensitive to noise and other imaging artefacts. This paper presents a hybrid version of KFCM with induced Gaussian spatial information. Sensitivity and specificity values of the proposed work are observed to be high and also the possibility of exudate misclassification is significantly reduced by the proposed method as compared to existing algorithms. Improvement: The frame work presented can be developed further by the inclusion of adaptive weights for the multiple kernels.
Diabetic Retinopathy, Exudates, FCM Clustering Algorithm, Multiple Kernel Induced Gaussian Spatial FCM Algorithm.
- Diabetic Retinopathy: What you should know. National Eye Institute, National Institute of Health; NIH Publication No.15-2171.
- Prentasic P, Loncaric S. Weighted ensemble based automatic detection of exudates in fundus photographs. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2014. p. 138–41. ISSN 1094-687X.
- Princye PH, Vijayakumari V. Detection of exudates and feature extraction of retinal images using fuzzy clustering method. IEEE Third International Conference on Computational Intelligence and Information Technology (CIIT); 2013. p. 388–94.
- Singh A, Sengar N, Dutta MK, Riha K, Minar J. Automatic exudates detection in fundus image using intensity thresholding and morphology. Seventh ICUMT, IEEE; 2015. p.330–4.
- Ravindraiah R, Rajendra Prasad P, Chandra Mohan Reddy S. Detection of exudates in Diabetic Retinopathy images using Laplacian kernel induced spatial FCM clustering algorithm. INDJST. 2016; 9(15).
- Sundaresan V, Ram K, Joshi N, Sivaprakasam M, Gandhi R. Computer-assisted grading of diabetic macular edema on retinal color fundus images. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015. p. 4330–3. ISSN-1094-687X.
- Naqvi SAG, Zafar MF, I ul Haq I. Referral system for hard exudates in eye fundus. Computers in Biology and Medicine. 2015; 64:217–35.
- Zhang X, Thibault G, Decenciere E, Marcotegui B. Exudate detection in color retinal images for mass screening of Diabetic Retinopathy. Medical Image Analysis. 2014; 18(7):1026–43.
- Reshma Chand C P, Dheeba J. Automatic detection of exudates in color fundus retinopathy images. INDJST, 2015; 8(26). ISSN (Print): 0974-6846.
- Nijalingappa P, Sandeep B. Machine learning approach for the identification of Diabetes Retinopathy and its stages.IEEE International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT); 2015. p. 653–8.
- Hassan HA, Tahir NM, Yassin I, Yahaya CHC, Shafie SM.Visualisation of exudates in fundus images using radar chart and color auto correlogram technique. IEEE International Conference on Computer Vision and Image Analysis Applications (ICCVIA); 2015. p. 1–6.
- Luangruangrong W, Kulkasem P, Rasmequan S, Rodtook A, Chinnasarn K. Automatic exudates detection in retinal images using efficient integrated approaches. IEEE AsiaPacific Signal and Information Processing Association Annual Summit and Conference (APSIPA); 2014. p. 1–5.
- Cyriac M, Karthik B. Detection of optical disc and exudates in colour fundus images. INDJST, 2015 Nov; 8(32).
- Ravindraiah R, Chandra Mohan Reddy S. Qualitative evaluation of fuzzy clustering methods in segmentation of fundoscope Diabetic Retinopathy images. GESJ: Computer Science and Telecommunications. 2015; 2(46).
- Chen L, Philip Chen CL, Mingzhu LuM. A multiple-kernel fuzzy c-means algorithm for image segmentation. IEEE Systems, Man and Cybernetics Society; 2011. p. 1263–74.
- Biniaz A, Abbassi A, Shamsi M, Ebrahimi A. Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation. IEEE 19th Iranian Conference on Biomedical Engineering (ICBME); 2012. p. 154–8.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.