Abstract
Retinal vasculature of the human circulatory system which can be visualized directly provides a number of systemic conditions and can be diagnosed by the detection of lesions. Changes in these structures are found to be correlated with pathological conditions and provide information on severity or state of various diseases. In this work, particle swarm optimization algorithm based multilevel thresholding is adopted for detecting the vasculature structures in retinal fundus images. Initially, adaptive histogram equalization is used for pre-processing of the original images. Tsallis multilevel thresholding is used for the segmentation of the blood vessels. Further, similarity measures are used to quantify the similarity between the segmented result and the corresponding ground truth. The optimal multi-threshold selection using particle swarm optimization seems to provide better results. Similarity measures analysis using dendrogram and box plot provide validation of the segmentation procedure attempted.
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References
Muthu, R.K.M., Rajendra, A.U., Chua, K.C., Lim, C.M., Ng, E.Y.K., Milind, M.M., Augustinus, L.: Application of Intuitionistic Fuzzy Histon Segmentation for the Automated Detection of Optic Disc in Digital Fundus Images. In: IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong and Shenzhen, China (2012)
Radhika, C.: A Morphological approach for discrimination between Glaucomatous and Non glaucomatous Eyes by Optic Disc Localization. Int. J. Adv. Research in Comput. Engg.&Tech. 1(4), 643–648 (2012)
Kumar, K.V.V., Ravi, T., Rani, B.M.S., Habibullah, K.: Detection of Retinal Diseases by Tracing Vessel Trees and Accurately Segmenting Vessels. Int. J. of Engg. and Adv. Tech. 1(4), 182–187 (2012)
Osareh, A.: Automated identification of diabetic retinal exudates and the optic disc. PhD Thesis, Department of Computer Science, Faculty of Engineering, University of Bristol, Bristol (2004)
Gayathri, D.B., Balasubramanian, R.: Segmentation of Optic Disk from Fundus Images of the Human Eye. In: International Conference on Computing and Control Engineering, Chennai (2012)
Rattathanapad, S., Mittrapiyanuruk, P., Kaewtrakulpong, P., Uyyanonvara, B., Sinthanayothin, C.: Vessel Extraction in Retinal Images using Multilevel Line Detection. In: IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong and Shenzhen, China (2012)
Xiayu, X., Meindert, N., Qi, S., Milan, S., Mona, K.G.: Vessel Boundary Delineation on Fundus Images Using Graph-Based Approach. IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011)
Farnoosh, G., Seyed, M.Z., Hamid, R.P., Touka, B.: A Novel Method for Vessel Detection Using Contourlet Transform. In: IEEE Conference on Communications (2012)
Usman, A.M., Ibaa, J., Anam, T.: Blood Vessel Enhancement and Segmentation for Screening of Diabetic Retinopathy. TELKOMNIKA 10(2), 327–334 (2012)
Sushil, K., Tarun, K.S., Millie, P., Ray, A.K.: Adaptive Artificial Bee Colony for Segmentation of CT lung Images. Int. J. Comput. Appl. iRAFIT, 1–5 (2012)
Kavitha, G., Ramakrishnan, S.: Identification and Analysis of Macula in Retinal Images using Ant Colony Optimization based Hybrid Method. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 1174–1177 (2009)
Zhang, Y., Wu, L.: Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy 13, 841–859 (2011), doi:10.3390/e13040841
Sathya, P.D., Kayalvizhi, R.: Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Engg. Appns. of Arti. Intelli. 24, 595–615 (2011)
Zhang, X.F., Shang, J.K.: Application of Image Segmentation Algorithm Based on Particle Swarm Optimization and Rough Entropy Standard. In: Proc. of IEEE Conf. on Chinese Control and Decision Conference, pp. 2905–2909 (2009)
Yin, P.Y.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization algorithm. Appl. Math. Comput. 184(2), 503–513 (2007)
Sathya, P.D., Kayalvizhi, R.: Optimum Multilevel Image Thresholding Based on Tsallis Entropy Method with Bacterial Foraging Algorithm. Int. J. Comp. Sci. Issues (IJCSI) 7(5), 336–343 (2010)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Oxford University Press, Inc., New York (2001)
Anderberg, M.R.: Cluster analysis for applications. Academic Press, New York (1973)
Rosenburg, H.C.: Cluster analysis for researchers. Lifetime Learning Publications, Belmont (1984)
Choi, S.S., Cha, S.H., Tappert, C.: A Survey of Binary Similarity and Distance Measures. Journal on Systemics, Cybernetics and Informatics 8(1), 43–48 (2010)
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Raja, N.S.M., Kavitha, G., Ramakrishnan, S. (2012). Analysis of Vasculature in Human Retinal Images Using Particle Swarm Optimization Based Tsallis Multi-level Thresholding and Similarity Measures. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_45
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DOI: https://doi.org/10.1007/978-3-642-35380-2_45
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