Abstract
In this paper a method for pattern analysis in dermoscopic images of abnormally pigmented skin (melanocytic lesions) is presented. In order to diagnose a possible skin cancer, physicians assess the lesion according to different rules. The new trend in Dermatology is to classify the lesion by means of pattern irregularity. In order to analyze the pattern turbulence, lesions ought to be segmented into single pattern regions. Our classification method, when applied on overlapping lesion patches, provides a pattern chart that could ultimately allow for in-region single-texture turbulence analysis. Due to the color-textured appearance of these patterns, we present a novel method based on a Finite Symmetric Conditional Model (FSCM) Markov Random Field (MRF) color extension for the characterization and discrimination of pattern samples. Our classification success rate rises to 86%.
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References
Stolz, W., Braun-Falco, O., Bilek, P., Landthaler, M., Burgdorf, W.H.C., Cognetta, A.B.: Color Atlas of Dermatoscopy. Blackwell Wissenschafts-Verlag, Berlin (2002)
Westerhoff, K., McCarthy, W.H., Menzies, S.W.: Increase in the sensitivity for melanoma diagnosis by primary care physicians using skin surface microscopy. British Journal of DermatologyĀ 143(5), 1016ā1020 (2000)
Binder, M., Kittler, H., Seeber, A., Steiner, A., Pehamberger, H., Wolff, K.: Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network. Melanoma ResearchĀ 8(3), 261ā266 (1998)
Schmidt, P.: Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Transactions on Medical ImagingĀ 18(2), 164ā171 (1999)
Schmid-Saugeon, P., Guillod, J., Thiran, J.P.: Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and GraphicsĀ 27(1), 65ā78 (2003)
Stoecker, W.V., Li, W.W., Moss, R.H.: Automatic detection of asymmetry in skin tumors. Computerized Medical Imaging and GraphicsĀ 16(3), 191ā197 (1992)
Erkol, B., Moss, R.H., Stanley, R.J., Stoecker, W.V., Hvatum, E.: Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Research and TechnologyĀ 11(1), 17ā26 (2005)
Lee, T.K., Claridge, E.: Predictive power of irregular border shapes for malignant melanomas. Skin Research and TechnologyĀ 11(1), 1ā8 (2005)
Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S.: A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions. IEEE Transactions on Medical ImagingĀ 22(8), 959ā964 (2003)
Golston, J.E., Moss, R.H., Stoecker, W.V.: Boundary detection in skin tumor images: An overall approach and a radial search algorithm. Pattern RecognitionĀ 23(11), 1235ā1247 (1990)
Stanley, R.J., Moss, R.H., Stoecker, W.V., Aggawal, C.: A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images. Computerized Medical Imaging and GraphicsĀ 27(5), 387ā396 (2003)
Tommasi, T., Torre, E.L., Caputo, B.: Melanoma recognition using representative and discriminative kernel classifiers. In: Beichel, R.R., Sonka, M. (eds.) CVAMIA 2006. LNCS, vol.Ā 4241, pp. 1ā12. Springer, Heidelberg (2006)
Tanaka, T., Torii, S., Kabuta, I., Shimizu, K., Tanaka, M.: Pattern classification of nevus with texture analysis. IEEJ Transactions on Electrical and Electronic EngineeringĀ 3(1), 143ā150 (2008)
Serrano, C., Acha, B.: Pattern analysis of dermoscopic images based on markov random fields. Pattern RecognitionĀ 42(6), 1052ā1057 (2009)
Panjwani, D., Healey, G.: Results using random field models for the segmentation of color images of natural scenes, pp. 714ā719 (1995)
Kato, Z., Pong, T.C.: A markov random field image segmentation model for color textured images. Image and Vision ComputingĀ 24(10), 1103ā1114 (2006)
Tab, F.A., Naghdy, G., Mertins, A.: Scalable multiresolution color image segmentation. Signal ProcessingĀ 86(7), 1670ā1687 (2006)
Gao, J., Zhang, J., Fleming, M.G., Pollak, I., Cognetta, A.B.: Segmentation of dermatoscopic images by stabilized inverse diffusion equations, vol.Ā 3, pp. 823ā827 (1998)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Tokyo (2001)
Xia, Y., Feng, D., Zhao, R.: Adaptive segmentation of textured images by using the coupled markov random field model. IEEE Transactions on Image ProcessingĀ 15(11), 3559ā3566 (2006)
Kashyap, R.L., Chellappa, R.: Estimation and choice of neighbors in spatial-interaction models of images. IEEE Transactions on Information TheoryĀ IT-29(1), 60ā72 (1983)
Chen, Y., Hao, P.: Optimal transform in perceptually uniform color space and its application in image retrieval, vol.Ā 2, pp. 1107ā1110 (2004)
Manjunath, B.S., Simchony, T., Chellappa, R.: Stochastic and deterministic networks for texture segmentation. IEEE Transactions on Acoustics, Speech, and Signal ProcessingĀ 38(6), 1039ā1049 (1990)
Manjunath, B.S., Chellappa, R.: Unsupervised texture segmentation using markov random field models. IEEE Transactions on Pattern Analysis and Machine IntelligenceĀ 13(5), 478ā482 (1991)
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Mendoza, C.S., Serrano, C., Acha, B. (2009). Pattern Analysis of Dermoscopic Images Based on FSCM Color Markov Random Fields. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_63
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DOI: https://doi.org/10.1007/978-3-642-04697-1_63
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