Abstract
This paper describes a new approach to analyze the spectral information of the samples of skin tissue that are localized in the spatial plane of microscopic image for discrimination of three different skin cancerous lesion prognoses. First, a cancerous lesion image is segmented from the skin surface based on Otsu’s optimal histogram thresholding technique. This allows us to localize the abnormal area in the skin tissue that is affected most as compared to the surrounding cells that appear brighter in color. Color clusters of the segmented darker lesions are used to obtain the three-dimensional (3D) spectral distribution function in the (R, G, B) color space. The Maximum Likelihood (ML) parameter estimation is utilized for calculation of the mean vector and co-variance matrix of the Gaussian (or normal) density approximation of skin samples and with the Mahalonobis distance as similarity measure in the learning and the testing phases of the pattern recognition system.
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References
Housman, T.S., et al.: Skin cancer is among the most costly of all cancers to treat for the Medicare population. J. Am. Acad. Dermatol. 48(3), 425–429 (2003)
Dhinagar, N.J., Celenk, M., Akinlar, M.: Noninvasive screening and discrimination of skin images for early melanoma detection. In: IEEE iCBBE 2011, Wuhan, China (2011)
http://www.cancer.gov/cancertopics/pdq/screening/skin/Patient/page2
Dhinagar, N.J., Celenk, M.: Non-invasive detection and classification of skin cancer from visual and cross-sectional images. In: IEEE ISABEL 2011, Barcelona, Spain (2011)
Carpenter, P.M., Linden, K.G., McLaren, C.E.: Nuclear morphometry and molecular biomarkers of actinic keratosis, sun-damaged and nonexposed skin. Cancer Epdemiol. Biomarkers 13, 1996–2002 (2004)
Korde, V.R., et al.: Using optical coherence tomography to evaluate skin sun damage and precancer. Lasers in Surgery and Medicine 39, 687–695 (2007)
Harland, C.C., et al.: Differentiation of common benign pigmented skin lesions from melanoma by high-resolution ultrasound. British J. of Derm. 143, 281–289 (2000)
Taheri, S.: Decision support system – Skin cancer. Canadian J. on Image Processing and Computer Vision 2(2), 12–15 (2011)
Ballerini, L., Li, X., Fisher, R.B., Aldridge, B., Rees, J.: Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 312–319. Springer, Heidelberg (2010)
Schwartz, W.R., Pedrini, H.: Color Textured Image Segmentation Based on Spatial Dependence Using 3D Co-occurrence Matrices and Markov Random Fields. In: 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic, pp. 81–87 (2007)
Kato, Z., Pong, T.C., Song, G.Q.: Unsupervised segmentation of color textured images using a multi-layer MRF model. In: Proc. Int. Conf. Image Processing, Barcelona, Spain, pp. 961–964 (2003)
Hernandez, O.J., Khotanzad, A.: Color Image Segmentation Using Multispectral Random Field Texture Model & Color Content Features. JCS&TÂ 4(3) (2004)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber. 9, 62–66 (1997)
Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. 1 (1992)
Celenk, M., Conley, T., Willis, J., Graham, J.: Predictive network anomaly detection and visualization. IEEE Trans. on Info. For. and Sec. 5(2), 288–299 (2010)
Swain, M.J., Ballard, D.H.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press (2009)
Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice Hall (1990)
Dhinagar, N.J., Celenk, M.: Power spectra based classification of cancerous nevoscope skin images. In: IEEE ICCAIE 2011, Penang, Malaysia (2011)
http://obel.ee.uwa.edu.au/research/skin/melanoma/gallery.html
Harland, C.C., Kale, S.G., Jackson, P., Mortimer, P.S., Bamber, J.C.: Differentiation of common benign pigmented skin lesions from melanoma by high-resolution ultrasound. British Journal of Dermatology 143, 281–289 (2000)
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Dhinagar, N.J., Glasgo, I., Celenk, M., Akinlar, M.A. (2012). Cancerous Lesion Detection from Nevoscope Skin Surface Images via Parametric Color Clustering. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_46
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DOI: https://doi.org/10.1007/978-3-642-33506-8_46
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