Author : Naluguru Udaya Kumar 1
Date of Publication :7th March 2017
Abstract: One of the major diseases that cause severe threat to the eye is named as Diabetic retinopathy. It creates thereby blindness among the people in the small age itself. The retinal image processing is used for analyzing and detecting the disease with the retinal blood vessels. By analysing and detecting of vasculature structures in retinal images, The most widely recognized manifestations of diabetic retinopathy incorporate cotton fleece spots, hemorrhages, hard exudates and enlarged retinal veins. A patient with diabetic retinopathy malady needs to experience intermittent screening of retina.we can early detect the diabetes in advanced stages by comparison of its states of retinal blood vessels. In this paper, we present blood vessel segmentation approach, which can be used in computer based retinal image analysis to extract the retinal image vessels. Mathematical morphology and FCM clustering are used to segment the vessels. To enhance the blood vessels and suppress the background information, we perform smoothing and sharpning operation on the retinal image using mathematical morphology. Then the enhanced image is segmented using FCM-means clustering algorithm.The main focus of this proposed work is to design the algorithm based on segmentation with clustering,for detection of Retinal blood vessel with the help of MATLAB with maximum accuracy.The proposed approach is tested on the DRIVE dataset and is compared with alternative approaches.Experimental results obtained by the proposed approach showed that it is effective as it achieved best accuracy of 98.23%
Reference :
-
[1] Sebag, J. (2004): "Anomalous posterior vitreous detachment: a unifying concept in vitreo-retinal disease." Graefe's archive for clinical and experimental ophthalmology 242, no. 8 690-698.
[2]Sebag, J. "Anomalous posterior vitreous detachment: a unifying concept in vitreo-retinal disease." Graefe's archive for clinical and experimental ophthalmology 242, no. 8 (2004): 690-698.
[3] Adi, K. G., Rao, P. V., & Adi, V. K. (2015). Analysis and Detection of Cholesterol by Wavelets based and ANN Classification. Procedia Materials Science, 10, 409-418.
[4]Shen, S., Sandham, W. A., & Granat, M. H. (2003, April). Preprocessing and segmentation of brain magnetic resonance images. In Information Technology Applications in Biomedicine, 2003. 4th International IEEE EMBS Special Topic Conference on (pp. 149-152). IEEE.
[5] Norton, E. W., Aaberg, T., Fung, W., & Curtin, V. T. (1969). Giant retinal tears. I. Clinical management with intravitreal air. Transactions of the American Ophthalmological Society, 67, 374.
[6] Rale, A. P., Gharpure, D. C., & Ravindran, V. R. (2009, December). Comparison of different ANN techniques for automatic defect detection in X-ray images. In Emerging Trends in Electronic and Photonic Devices & Systems, 2009. ELECTRO'09. International Conference on (pp. 193-197). IEEE.
[7] Miao, Q., Xu, P., Liu, T., Song, J., & Chen, X. (2015). A novel fast image segmentation algorithm for large topographic maps. Neurocomputing, 168, 808-822.
[8] ZHENG, H. L., ZHOU, X. Z., & WANG, J. Y. (2003). Automatic color segmentation of topographic maps based on the combination of spatial relation information and color information. Journal of Image and Graphics, 3, 017.
[9] Mignotte, M. (2008). Segmentation by fusion of histogram-based-means clusters in different color spaces. Image Processing, IEEE Transactions on,17(5), 780-787.
[10] HARRABI, R., & BRAIEK, E. B. Color Image Segmentation by Multilevel Thresholding using a Two Stage Optimization Approach and Fusion.
[11] Narkbuakaew, W., Nagahashi, H., Aoki, K., & Kubota, Y. (2014). Bone Segmentation in CT-Liver Images Using K-Means Clustering for 3D Rib Cage SurfaceModeling. WSEAS Transactions on Biology and Biomedicine, 11, 183-193.
[12] Comaniciu, D., & Meer, P. (1997, June). Robust analysis of feature spaces: color image segmentation. In Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on (pp. 750-755). IEEE.
[13] Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2, 849-856.
[14] Chen, S. H., & Chen, W. Y. (1995). Generalized minimal distortion segmentation for ANN-based speech recognition. IEEE Transactions on Speech and Audio Processing, 3(2), 141-145.
[15] Boone, D. S., & Roehm, M. (2002). Retail segmentation using artificial neural networks. International journal of research in marketing, 19(3), 287-301.
[16] Awad, M., Chehdi, K., & Nasri, A. (2007). Multicomponent image segmentation using a genetic algorithm and artificial neural network.Geoscience and Remote Sensing Letters, IEEE, 4(4), 571-575.
[17] Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, (3), 31-44.
[18] Khotanzad, A., & Bouarfa, A. (1990). Image segmentation by a parallel, non-parametric histogram based clustering algorithm. Pattern Recognition, 23(9), 961-973.
[19] M. Moazam Fraz, Alicja R. Rudnicka,Christopher G. Owen and Sarah A. Barman, “Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification, ” in International journal of computer assisted radiology and surgery, pp. 1-17, (2013).
[20] Y. Yin, M. Adel, and S. Bourennane, “Automatic Segmen- tation and Measurement of Vasculature in Retinal Fundus Images Using Probabilistic Formulation, ” in Computational and mathematical methods in medicinel,Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine ,vol.2013, pp. 16, (2013).
[21] M.S. Miri and A. Mahloojifar, “An approach to local- ize the retinal blood vessels using bit planes and center- line detection, ” in Computer methods and programs in biomedicine,vol.108, pp. 600-616, (2012)