Computerized Medical Diagnosis of Melanocytic Lesions based on the ABCD approach

Melanoma is a type of skin cancer and is caused by the uncontrolled growth of atypical melanocytes. In recent decades, computer aided diagnosis is used to support medical professionals; however, there is still no globally accepted tool. In this context, similar to state-of-the-art we propose a system that receives a dermatoscopy image and provides a diagnostic if the lesion is benign or malignant. This tool is composed with next modules: Preprocessing, Segmentation, Feature Extraction, and Classification. Preprocessing involves the removal of hairs. Segmentation is to isolate the lesion. Feature extraction is considering the ABCD dermoscopy rule. The classification is performed by the Support Vector Machine. Experimental evidence indicates that the proposal has 90.63 % accuracy, 95 % sensitivity, and 83.33 % specificity on a data-set of 104 dermatoscopy images. These results are favorable considering the performance of diagnosis by traditional progress in the area of dermatology.

[1]  Gerard de Haan,et al.  Automatic imaging sysem with decision support for inspection of pigmented skin lesions and melanoma diagnosis. , 2009 .

[2]  Giovanni Mettivier,et al.  Digital Imaging Processing , 2010 .

[3]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[4]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[5]  Jorge S. Marques,et al.  Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features , 2014, IEEE Systems Journal.

[6]  Carlos A. Cattaneo,et al.  Métodos de Umbralización de Imágenes Digitales Basados en Entropía de Shannon y Otros , 2011 .

[7]  Eduardo Valle,et al.  Statistical Learning Approach for Robust Melanoma Screening , 2014, 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images.

[8]  Criterios dermatoscópicos para el diagnóstico del melanoma , 2002 .

[9]  Qaisar Abbas,et al.  Pattern classification of dermoscopy images: A perceptually uniform model , 2013, Pattern Recognit..

[10]  G. Argenziano,et al.  Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. , 1998, Archives of dermatology.

[11]  Randy H. Moss,et al.  A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..

[12]  Gerald Schaefer,et al.  Skin lesion segmentation using an improved snake model , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[13]  Rajvi Parikh,et al.  A Survey on Computer Vision Based Diagnosis for Skin Lesion Detection , 2013 .

[14]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[15]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[16]  Junji Maeda,et al.  Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[17]  G. Argenziano,et al.  Dermoscopy of pigmented skin lesions. , 2001, European journal of dermatology : EJD.

[18]  Harold S Rabinovitz,et al.  Dermoscopy of pigmented skin lesions. , 2005, Journal of the American Academy of Dermatology.

[19]  Margarita Cunill-Rodríguez,et al.  An algorithm for the characterization of digital images of pigmented lesions of human skin , 2014, Optics & Photonics - Optical Engineering + Applications.

[20]  S. Menzies,et al.  A sensitivity and specificity analysis of the surface microscopy features of invasive melanoma , 1996, Melanoma research.

[21]  Estudio dermoscpico del melanoma maligno cutneo: anlisis descriptivo de 45 casos , 2008 .

[22]  Harald Ganster,et al.  Automated Melanoma Recognition , 2001, IEEE Trans. Medical Imaging.

[23]  Enrico Blanzieri,et al.  A multiple classifier system for early melanoma diagnosis , 2003, Artif. Intell. Medicine.

[24]  J. Scharcanski,et al.  Computer Vision Techniques for the Diagnosis of Skin Cancer , 2013 .

[25]  Maurílio Boaventura,et al.  A well-balanced flow equation for noise removal and edge detection , 2003, IEEE Trans. Image Process..

[26]  Pang-Ning Tan,et al.  kNN: k-Nearest Neighbors , 2009 .

[27]  Germán Capdehourat,et al.  Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions , 2011, Pattern Recognit. Lett..

[28]  R. H. Moss,et al.  Real‐time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location , 2015, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[29]  Rainer Hofmann-Wellenhof,et al.  Color Atlas of Melanocytic Lesions of the Skin , 2008 .

[30]  Bipin C. Desai,et al.  A multiple expert-based melanoma recognition system for dermoscopic images of pigmented skin lesions , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[31]  R P Braun,et al.  Three‐point checklist of dermoscopy: an open internet study , 2006, The British journal of dermatology.

[32]  D. Stoffer,et al.  Bootstrapping State-Space Models: Gaussian Maximum Likelihood Estimation and the Kalman Filter , 1991 .