Abstract
In this paper we propose a clustering technique for the recognition of pigmented skin lesions in dermatological images. It is known that computer vision-based diagnosis systems have been used aiming mostly at the early detection of skin cancer and more specifically the recognition of malignant melanoma tumour. The feature extraction is performed utilising digital image processing methods, i.e. segmentation, border detection, colour and texture processing. The proposed method belongs to a class of clustering algorithms which are very successful in dealing with high dimensional data, utilising information driven by the Principal Component Analysis. Experimental results show the high performance of the algorithm against other methods of the same class.
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References
Boley, D.: Principal direction divisive partitioning. Data Mining and Knowledge Discovery 2(4), 325–344 (1998)
Chung, D.H., Sapiro, G.: Segmenting skin lesions with partial-differential-equations-based image processing algorithms. IEEE Transactions on Medical Imaging 19(7), 763–767 (2000)
Dhillon, I., Kogan, J., Nicholas, C.: Feature selection and document clustering. A Comprehensive Survey of Text Mining, 73–100 (2003)
Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 269–274. ACM, New York (2001)
Greengard, L., Strain, J.: The fast gauss transform. SIAM J. Sci. Stat. Comput. 12(1), 79–94 (1991)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999), citeseer.ist.psu.edu/jain99data.html
Maglogiannis, I.: Automated segmentation and registration of dermatological images, vol. 2, pp. 277–294. Springer, Heidelberg (2003)
Maglogiannis, I., Pavlopoulos, S., Koutsouris, D.: An integrated computer supported acquisition, handling and characterization system for pigmented skin lesions in dermatological images. IEEE Transactions on Information Technology in Biomedicine 9(1), 86–98 (2005)
Maglogiannis, I., Doukas, C.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Transactions on Information Technology in Biomedicine 13(5), 721–733 (2009)
Marks, R.: Epidemiology of melanoma. Clin. Exp. Dermatol. 25, 459–463 (2000)
Nachbar, F., Stolz, W., Merkle, T., Cognetta, A.B., Vogt, T., Landthaler, M., Bilek, P., Braun-Falco, O., Plewig, G.: The abcd rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions. J. Amer. Acad. Dermatol. 30(4), 551–559 (1994)
Nilsson, M.: Hierarchical Clustering using non-greedy principal direction divisive partitioning. Information Retrieval 5(4), 311–321 (2002)
Pariser, R., Pariser, D.: Primary care physicians errors in handling cutaneous disorders. J. Am. Acad. Dermatol. 17(3), 239–245 (1987)
Sanders, J.E., Goldstein, B.S., Leotta, D.F., Richards, K.A.: Image processing techniques for quantitative analysis of skin structures. Computer Methods and Programs in Biomedicine 59(3), 167–180 (1999)
Stoecker, W.V., Li, W.W., Moss, R.H.: Automatic detection of asymmetry in skin tumors. Computerized Med. Imag. Graph 16(3), 191–197 (1992)
Tasoulis, S.K., Plagianakos, V.P., Tasoulis, D.K.: Projection based clustering at gene expression data. In: Computational intelligence methods for bioinformatics and biostatistics, Genova, Italy (2009)
Tasoulis, S., Tasoulis, D.: Improving principal direction divisive clustering. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), Workshop on Data Mining using Matrices and Tensors (2008)
Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning 55(3), 311–331 (2004)
Yang, C., Duraiswami, R., Gumerov, N.A., Davis, L.: Improved fast gauss transform and efficient kernel density estimation. In: Proceedings of Ninth IEEE International Conference on Computer Vision 2003, pp. 664–671 (2003)
Zeimpekis, D., Gallopoulos, E.: Principal direction divisive partitioning with kernels and k-means steering. In: Survey of Text Mining II: Clustering, Classification, and Retrieval, pp. 45–64 (2007)
Zhang, Z., Stoecker, W., Moss, R.: Border detection on digitized skin tumor image. IEEE Transactions on Medical Imaging 19(11), 1128–1143 (2000)
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Tasoulis, S.K., Doukas, C.N., Maglogiannis, I., Plagianakos, V.P. (2010). Skin Lesions Characterisation Utilising Clustering Algorithms. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_28
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DOI: https://doi.org/10.1007/978-3-642-12842-4_28
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