Abstract
Skin cancer is the most common of all cancer types and Malignant Melanoma is the most dangerous form of it, thus prevention is vital. Risk assessment of skins lesions is usually done through the ABCD rule (asymmetry, border, color and differential structures) that classifies the lesion as benign, suspicious or highly suspicious of Malignant Melanoma. A methodology to assess the asymmetry of a skin lesion image in relation to each axis of inertia, for both dermoscopic and mobile acquired images, is presented. It starts by extracting a set of 310 of asymmetry features, followed by testing several feature selection and machine learning classification methods in order to minimize the classification error. For dermoscopic images, the developed methodology achieves an accuracy of 87% regarding asymmetry classification while, for mobile acquired images the accuracy reaches 73.1%.
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References
Rajpara, S., Botello, A., Townend, J., Ormerod, A.: Systematic review of dermoscopy and digital dermoscopy/artificial intelligence for the diagnosis of melanoma. British Journal of Dermatology 161, 591–604 (2009)
Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artificial Intelligence in Medicine 56, 69–90 (2012)
Masood, A., Ali Al-Jumaily, A.: Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms. International Journal of Biomedical Imaging (2013)
Stolz, W., Riemann, A., Cognetta, A., Pillet, L., Abmayr, W., Holzel, D., Bilek, P., Nachbar, F., Landthaler, M.: Abcd rule of dermatoscopy-a new practical method for early recognition of malignant-melanoma. European Journal of Dermatology 4, 521–527 (1994)
Argenziano, G., Soyer, H.P., De Giorgio, V., Piccolo, D., Carli, P., Delno, M., Ferrari, A., Hofmann-Wellenhof, R., Massi, D., Mazzocchetti, G., et al.: Interactive atlas of dermoscopy (2000)
Mendonça, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: Ph 2-a dermoscopic image database for research and benchmarking. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5437–5440. IEEE (2013)
Fraunhofer, P.: Melanoma detection, internal project (2014), http://www.fraunhofer.pt/en/fraunhofer_aicos/projects/internal_research/melanoma_detection.html
Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Computerized Medical Imaging and Graphics 31, 362–373 (2007)
Cavalcanti, P.G., Scharcanski, J.: Macroscopic pigmented skin lesion segmentation and its inuence on lesion classification and diagnosis. In: Color Medical Image Analysis, pp. 15–39. Springer (2013)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 610–621 (1973)
Chang, W.Y., Huang, A., Yang, C.Y., Lee, C.H., Chen, Y.C., Wu, T.Y., Chen, G.S.: Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PloS One 8, e76212 (2013)
Gonzalez, R.C., Woods, R.E.: Digital image processing, pp. 132–134. Prentice Hall (2002)
Aswin, R.B., Jaleel, J.A., Salim, S.: Implementation of ann classifier using matlab for skin cancer detection. International Journal of Computer Science and Mobile Computing. ICMIC, 87–94 (2013)
Cheerla, N., Frazier, D.: Automatic melanoma detection using multi-stage neural networks. International Journal of Innovative Research in Science, Engineering and Technology 3, 9164–9183 (2014)
Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering 15, 1437–1447 (2003)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clus-tering. IEEE Transactions on Knowledge and Data Engineering 17, 491–502 (2005)
Alcon, J.F., Ciuhu, C., Ten Kate, W., Heinrich, A., Uzunbajakava, N., Krekels, G., Siem, D., De Haan, G.: Automatic imaging system with decision support for inspection of pig-mented skin lesions and melanoma diagnosis. IEEE Journal of Selected Topics in Signal Processing 3, 14–25 (2009)
Scharcanski, J., Celebi, M.E.: Computer vision techniques for the diagnosis of skin cancer (2013)
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Vasconcelos, M.J.M., Rosado, L., Ferreira, M. (2014). Principal Axes-Based Asymmetry Assessment Methodology for Skin Lesion Image Analysis. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_3
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DOI: https://doi.org/10.1007/978-3-319-14364-4_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
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