Dermatologist-like feature extraction from skin lesion for improved asymmetry classification in PH2 database

Asymmetry is one of key characteristics for early diagnosis of melanoma according to medical algorithms such as (ABCD, CASH etc.). Besides shape information, cues such as irregular distribution of colors and structures within the lesion area are assessed by dermatologists to determine lesion asymmetry. Motivated by the clinical practices, we have used Kullback-Leibler divergence of color histogram and Structural Similarity metric as a measures of these irregularities. We have presented performance of several classifiers using these features on publicly available PH2 dataset. The obtained result shows better asymmetry classification than available literature. Besides being a new benchmark, the proposed technique can be used for early diagnosis of melanoma by both clinical experts and other automated diagnosis systems.

[1]  V. Ng,et al.  Measuring asymmetries of skin lesions , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[2]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[3]  Massimo Ferri,et al.  Qualitative asymmetry measure for melanoma detection , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[4]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[5]  Philippe Schmid-Saugeona,et al.  Towards a computer-aided diagnosis system for pigmented skin lesions. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[6]  Ilias Maglogiannis,et al.  Overview of Advanced Computer Vision Systems for Skin Lesions Characterization , 2009, IEEE Transactions on Information Technology in Biomedicine.

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

[8]  C. Grana,et al.  Pigment distribution in melanocytic lesion images: a digital parameter to be employed for computer‐aided diagnosis , 2005, 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.

[9]  W V Stoecker,et al.  Automatic detection of asymmetry in skin tumors. , 1992, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[10]  Qiang Chen,et al.  Accurate and Scalable System for Automatic Detection of Malignant Melanoma , 2015 .

[11]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[12]  Susan M Swetter,et al.  Screening, early detection, and trends for melanoma: current status (2000-2006) and future directions. , 2007, Journal of the American Academy of Dermatology.

[13]  Philippe Schmid-Saugeon,et al.  Symmetry axis computation for almost-symmetrical and asymmetrical objects: Application to pigmented skin lesions , 2000, Medical Image Anal..

[14]  Mackie,et al.  Clinical accuracy of the diagnosis of cutaneous malignant melanoma , 1998, The British journal of dermatology.

[15]  Luís Rosado,et al.  A new risk assessment methodology for dermoscopic skin lesion images , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

[16]  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).

[17]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[18]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[19]  Masafumi Hagiwara,et al.  An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm , 2008, Comput. Medical Imaging Graph..

[20]  Luís Rosado,et al.  Principal Axes-Based Asymmetry Assessment Methodology for Skin Lesion Image Analysis , 2014, ISVC.

[21]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[22]  Begoña García Zapirain,et al.  Automated Detection of Melanoma in Dermoscopic Images , 2014 .