Dermoscopic diagnosis of melanoma in a 4D space constructed by active contour extracted features

Dermoscopy, also known as epiluminescence microscopy, is a major imaging technique used in the assessment of melanoma and other diseases of skin. In this study we propose a computer aided method and tools for fast and automated diagnosis of malignant skin lesions using non-linear classifiers. The method consists of three main stages: (1) skin lesion features extraction from images; (2) features measurement and digitization; and (3) skin lesion binary diagnosis (classification), using the extracted features. A shrinking active contour (S-ACES) extracts color regions boundaries, the number of colors, and lesion's boundary, which is used to calculate the abrupt boundary. Quantification methods for measurements of asymmetry and abrupt endings in skin lesions are elaborated to approach the second stage of the method. The total dermoscopy score (TDS) formula of the ABCD rule is modeled as linear support vector machines (SVM). Further a polynomial SVM classifier is developed. To validate the proposed framework a dataset of 64 lesion images were selected from a collection with a ground truth. The lesions were classified as benign or malignant by the TDS based model and the SVM polynomial classifier. Comparing the results, we showed that the latter model has a better f-measure then the TDS-based model (linear classifier) in the classification of skin lesions into two groups, malignant and benign.

[1]  Michael G. Madden,et al.  The Genetic Evolution of Kernels for Support Vector Machine Classifiers , 2004 .

[2]  B. Thiers,et al.  The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy , 2008 .

[3]  S. Menzies,et al.  Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. , 1996, Archives of dermatology.

[4]  K Wolff,et al.  Statistical evaluation of epiluminescence microscopy criteria for melanocytic pigmented skin lesions. , 1993, Journal of the American Academy of Dermatology.

[5]  Gerald Schaefer,et al.  Gradient vector flow with mean shift for skin lesion segmentation , 2011, Comput. Medical Imaging Graph..

[6]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[7]  Mutlu Mete,et al.  Fast density-based lesion detection in dermoscopy images , 2011, Comput. Medical Imaging Graph..

[8]  Angela Ferrari,et al.  Interactive atlas of dermoscopy , 2000 .

[9]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[10]  Nikolay Metodiev Sirakov,et al.  An Integral Active Contour Model for Convex Hull and Boundary Extraction , 2009, ISVC.

[11]  W. Stolz,et al.  The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.

[12]  Massimiliano Pontil,et al.  Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  M. Binder,et al.  Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. , 1995, Archives of dermatology.

[14]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[15]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Mutlu Mete,et al.  Lesion detection in demoscopy images with novel density-based and active contour approaches , 2010, BMC Bioinformatics.

[17]  R. Hofmann-Wellenhof,et al.  A support vector machine for decision support in melanoma recognition , 2010, Experimental dermatology.

[18]  Mutlu Mete,et al.  Automatic boundary detection and symmetry calculation in dermoscopy images of skin lesions , 2011, 2011 18th IEEE International Conference on Image Processing.

[19]  M. Stella Atkins,et al.  A novel method for detection of pigment network in dermoscopic images using graphs , 2011, Comput. Medical Imaging Graph..

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  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.

[22]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[23]  Gerald Schaefer,et al.  Lesion border detection in dermoscopy images , 2009, Comput. Medical Imaging Graph..

[24]  K. Mulchrone,et al.  Fitting an ellipse to an arbitrary shape: implications for strain analysis , 2004 .

[25]  Giuseppe Argenziano,et al.  Three-point checklist of dermoscopy. A new screening method for early detection of melanoma. , 2004, Dermatology.

[26]  Emmanuel Viennet,et al.  A Convex Active Contour Region-Based Model for Image Segmentation , 2011, CAIP.

[27]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.