Optimal selection of features using wavelet fractal descriptors and automatic correlation bias reduction for classifying skin lesions

Abstract The non-invasive computerized image analysis techniques have a great impact on accurate and uniform evaluation of skin abnormalities. The paper reports a method for the texture and morphological feature extraction from skin lesion images to differentiate common melanoma from benign nevi. In this work, a 2D wavelet packet decomposition (WPD) based fractal texture analysis has been proposed to extract the irregular texture pattern of the skin lesion area. On the whole 6214 features have been extracted from each of the 4094 skin lesion images, by analyzing the textural pattern and morphological structure of the lesion area. For the identification of the most efficient feature set, an improved correlation bias reduction method has been introduced in combination with support vector machine recursive feature elimination (SVM-RFE). An automatic selection of correlation threshold value has been introduced in this proposed work to eliminate the correlation bias problem associated with SVM-RFE algorithm. With these selected features, the support vector machine (SVM) classifier with radial basis function is found to achieve the classification performance of 97.63% sensitivity, 100% specificity and 98.28% identification accuracy. The results show that the scheme presented in this paper surpasses the performance of the other state-of-the art techniques for the differentiation of melanoma from other skin abnormalities.

[1]  Odemir Martinez Bruno,et al.  Texture analysis by fractal descriptors over the wavelet domain using a best basis decomposition , 2016 .

[2]  Thomas Roß,et al.  Feature selection for optimized skin tumor recognition using genetic algorithms , 1999, Artif. Intell. Medicine.

[3]  Kenneth Falconer,et al.  Fractal Geometry: Mathematical Foundations and Applications , 1990 .

[4]  R. Marks,et al.  An overview of skin cancers. Incidence and causation. , 1995, Cancer.

[5]  K Wolff,et al.  In vivo epiluminescence microscopy: improvement of early diagnosis of melanoma. , 1993, The Journal of investigative dermatology.

[6]  Zhen Ma,et al.  A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model , 2016, IEEE Journal of Biomedical and Health Informatics.

[7]  Salim Lahmiri,et al.  Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques , 2017, Biomed. Signal Process. Control..

[8]  Atam P. Dhawan,et al.  Classification of melanoma using tree structured wavelet transforms , 2003, Comput. Methods Programs Biomed..

[9]  T. Higuchi.  Approach to an irregular time series on the basis of the fractal theory , 1988 .

[10]  Matti Pietikäinen,et al.  Computer Vision Using Local Binary Patterns , 2011, Computational Imaging and Vision.

[11]  Franck Marzani,et al.  Automatic differentiation of melanoma from dysplastic nevi , 2015, Comput. Medical Imaging Graph..

[12]  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.

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

[14]  Reda Kasmi,et al.  Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule , 2016, IET Image Process..

[15]  Tao Zhang,et al.  Automatic epileptic EEG detection using DT-CWT-based non-linear features , 2017, Biomed. Signal Process. Control..

[16]  Robin Marks M.B.B.S. M.P.H..  An overview of skin cancers , 1995 .

[17]  U. Rajendra Acharya,et al.  Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm , 2017, Biomed. Signal Process. Control..

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

[19]  G. Jemec,et al.  Handbook of non-invasive methods and the skin, second edition , 2006 .

[20]  Kostas Delibasis,et al.  Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy , 2015, Comput. Methods Programs Biomed..

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

[22]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[23]  Niloofar Gheissari,et al.  Segmentation of Dermoscopy Images Using Wavelet Networks , 2013, IEEE Transactions on Biomedical Engineering.

[24]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  M. G. Fleming,et al.  Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. , 2003, Journal of the American Academy of Dermatology.

[26]  João Manuel R. S. Tavares,et al.  A computational approach for detecting pigmented skin lesions in macroscopic images , 2016, Expert Syst. Appl..

[27]  B. Mandelbrot,et al.  Fractals: Form, Chance and Dimension , 1978 .

[28]  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.

[29]  G. A. Edgar.  Measure, Topology, and Fractal Geometry , 1990 .

[30]  D. Dey,et al.  Mathematical morphology aided shape, texture and colour feature extraction from skin lesion for identification of malignant melanoma , 2015, 2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON).

[31]  C L Jones,et al.  Wavelet packet fractal analysis of neuronal morphology. , 2001, Methods.

[32]  James Bailey,et al.  Computer-Aided Diagnosis of Melanoma Using Border- and Wavelet-Based Texture Analysis , 2012, IEEE Transactions on Information Technology in Biomedicine.

[33]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[34]  David Zhang,et al.  Feature selection and analysis on correlated gas sensor data with recursive feature elimination , 2015 .

[35]  Qaisar Abbas,et al.  Unsupervised skin lesions border detection via two-dimensional image analysis , 2011, Comput. Methods Programs Biomed..

[36]  Nirupam Sarkar,et al.  An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[37]  Thomas Lengauer,et al.  Classification with correlated features: unreliability of feature ranking and solutions , 2011, Bioinform..

[38]  Robin Marks,et al.  An overview of skin cancers , 1995, Cancer.

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

[40]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[41]  F. Hausdorff.  Dimension und äußeres Maß , 1918 .

[42]  Masaru Tanaka,et al.  Four-Class Classification of Skin Lesions With Task Decomposition Strategy , 2015, IEEE Transactions on Biomedical Engineering.

[43]  M. J. Katz,et al.  Fractals and the analysis of waveforms. , 1988, Computers in biology and medicine.