Pigmented skin lesion diagnosis using geometric and chromatic features

Skin cancer appears to be one of the most dangerous types among others by the presence of malignant melanoma as one of pigmented skin lesion forms. Automated system for the purpose of pigmented skin lesion diagnosis mentioned through that paper is recommended as a non-invasive diagnosis tool. To obviate the problem of qualitative interpretation, two different image sets are used to examine the proposed system, a set of images acquired by standard camera (clinical images) and another set of dermoscopic images captured from the magnified dermoscope. Images are enhanced and segmented to separate the lesion from the background. Different geometric and chromatic features are extracted from the region of interest resulting from segmentation process. Then, the most prominent features that can cause an effect are selected by different selection methods; which are the Fisher score ranking and the t-test method. Most prominent features were introduced to two different classifiers; artificial neural network and Support vector machine for the discrimination of the two groups of lesions. System performance was measured regarding Specificity, Sensitivity and Accuracy. The artificial neural network designed with the combined geometric and chromatic features selected by fisher score ranking enabled a diagnostic accuracy of 95% for dermoscopic and 93.75% for clinical images.

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

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

[3]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[4]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[5]  N. Karssemeijer,et al.  Segmentation of suspicious densities in digital mammograms. , 2001, Medical physics.

[6]  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.

[7]  Tim K. Lee,et al.  MEASURING BORDER IRREGULARITY AND SHAPE OF CUTANEOUS MELANOCYTIC LESIONS , 2001 .

[8]  Alfredo Paolillo,et al.  Towards an automatic diagnosis system for skin lesions: Estimation of blue-whitish veil and regression structures , 2009, 2009 6th International Multi-Conference on Systems, Signals and Devices.

[9]  Ron Borland,et al.  What features do patients notice that help to distinguish between benign pigmented lesions and melanomas?: the ABCD(E) rule versus the seven-point checklist , 2005, Melanoma research.

[10]  I. Maglogiannis,et al.  Classification of dermatological images using advanced clustering techniques , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[11]  Alessandro Verri.  Support Vector Machines for 3-D Object RecognitionMassimiliano PontilCenter for Biological and Computational Learning , 1998 .

[12]  William M. Campbell,et al.  Support vector machines for speaker verification and identification , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

[13]  Tarun Kumar,et al.  A Theory Based on Conversion of RGB image to Gray image , 2010 .

[14]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[15]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[16]  S W Menzies.  Surface microscopy of pigmented skin tumours , 1997, The Australasian journal of dermatology.

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

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

[19]  G. Argenziano,et al.  Clinical and dermatoscopic criteria for the preoperative evaluation of cutaneous melanoma thickness. , 1999, Journal of the American Academy of Dermatology.

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

[21]  John Mantas,et al.  Computer-aided detection of clustered microcalcifications in digital mammograms , 2002 .

[22]  G Rassner,et al.  Clinical and Laboratory Investigations Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions , 2004 .

[23]  Mai S. Mabrouk,et al.  Computer Aided Diagnosis of Melanoma Skin Cancer using Clinical Photographic Images , 2013, BIOINFORMATICS 2013.

[24]  Vladimir Vezhnevets,et al.  A Survey on Pixel-Based Skin Color Detection Techniques , 2003 .

[25]  Adel Al-Jumaily,et al.  Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

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

[27]  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.

[28]  B.G. Zapirain,et al.  Skin cancer parameterisation algorithm based on epiluminiscence image processing , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[29]  Bushra Jalil,et al.  Multispectral Image Processing Applied to Dermatology , 2008 .