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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 2, Pages 711-732, DOI: https://doi.org/10.21307/ijssis-2017-562
License : (CC BY-NC-ND 4.0)
Received Date : 15-January-2013 / Accepted: 28-March-2013
This paper presents a study in an efficient methodology for analysis and characterization of digital images psoriasis lesions using Daubechies D8 wavelet technique. The methodology is based on the transformation of 2D Discrete Wavelet Transform (DWT) algorithm for Daubechies D8 at first level to obtain the coefficients of the approximations and details sub-images. For classification method, statistical approach analysis is applied to identify significance difference between each groups of psoriasis in terms of mean and standard deviation parameter. Results performances are concluded by observing the error plots with 95% confidence interval and applied independent T-test. The test outcomes have shown that approximate mean and standard deviation parameter can be used to distinctively classify erythroderma from the other groups in consistent with visual observations of the error plots. Whilst, in order to discriminate guttate from the other groups, standard deviation parameters for horizontal, vertical and diagonal can be utilized. Based on the results, plaque is distinguishable with guttate and erythroderma by using standard deviation vertical sub-images parameter. Results of Daubechies D8 is compared with study done previously by using Daubechies D4 and Daubechies D12 in order to observe the reliability of the results in Daubechies families. The resultant parameters can be used to design computer-aided system in diagnosis the skin lesion of psoriasis.
 I.G. Maglogianis, E.P. Zafiropoulos, “Characterization of Digital Medical Images Utilizing Support Vector Machines”, BMC Medical Informatics and Decision Making, 4:4, 2004.
 “Evaluation of Fingerprint Recognition Technologies-Bio-Finger,” Public Final Report, Version 1.1, Bundesamt f¨ur Sicherheit in der Informationstechnik, pp. 122, 2004.
 A. K. Jain, P. Flynn, and A. A. Ross, “Handbook of Biometrics”, Springer, New York, 2008.
 R. M. Bolle, J. H. Connell, S. Pankanti,N. K. Ratha, and A. W. Senior, “Guide to Biometrics”, Springer, New York, 2004.
 W. D. James, T. G. Berger, and D. M. Elston, “Andrew’s Diseases of the Skin-Clinical Dermatology”, Elsevier Saunders, 10th edition, 2006.
 T. P. Habif, “Clinical Dermatology”, Mosby, Hong Kong, 4th Edition, 2004.
 Giardina, E.C. Sinibaldi and G. Novelli, “The Psoriasis Genetics as a Model of Complex Disease”, Current Drug Targets-Inflammation and Allergy, 3(2): pp129-136, 2004.
 Nidhal K. Al Abbadi, Nizar Saadi Dahir, Muhsin A. Al-Dhalimi and Hind Restom, “Psoriasis Detection Using Skin Color and Texture Features”, Journal of Computer Science 6 (6): pp648-652, 2010.
 T. Morrow, "Evaluating New Therapies for Psoriasis”, Managed Care, Vol. 13, pp. 34-40, 2004.
 D. M. Pariser, "Management of Moderate to Severe Plaque Psoriasis With Biologic Therapy", Managed Care, Vol. 12, pp. 36-44, 2003.
H. Hashim, M.T. Ali, and N.A. Talib, "Skin Lesions Color Analysis Based on RGB Reflectance Indices", International Conference on New Techniques in Pharmaceutical and Biomedical Research, Malaysia 2005.
 M. Zafran, H.Hashim, Robbaiyah, Yuslindawati, “Identification of Psoriasis Lesion Features Using Daubechies D4 Wavelet Technique”, Recent Researches in Communications, Electronics, Signal Processing and Automatic Control, 2012.
 J. S. Taur, G. H. Lee, C. W. Tao, C. C. Chen, and C. W. Yang, " Segmentation of Psoriasis Vulgaris Images Using Multiresolution-Based Orthogonal Subspace Techniques", IEEE Transaction on Systems, Man and Cybernatics - Part B: Cybernatics, Vol. 36, No. 2, 2006.
 Djeddi Meriem, Ouahabi Abdeldjalil, Batatia Hadj, Basarab Adrian and Kouamé Denis, "Discrete Wavelet for Multifractal Texture Classification: Application to Medical Ulrasound Imaging", 2010 IEEE 17th International Conference on Imaging Processing, Hong Kong, 2010.
 S. Guyot, M-C. Peron and E. Deléchelle, “Spatial Speckle Characterization by Brownian Motion Analysis,” Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, Vol. 70, Issue 4, pp.046618.1-046618.8, 2004.
 Ahmad Fadzil M Hani, Esa Prakasa, Hurriyatul Fitriyah, Hermawan Nugroho, Azura Mohd Affandi and Suraiya Hani Hussein, "High Order Polynominal Surface Fitting for Measuring Roughness of Psoriasis Lesion", Visual Informatics: Sustaining Research and Innovation, pp 341-351, 2011.
 W. Marco, G. Armin, W. Christian, B. Patrick, S.Josef, “Discrimination of Benign Common Nevi from Malignant Malonoma Lesion by Use of Features Based on Spectral Properties of the Wavelet Transform”, Analytical and Quantitative Cytology and Historlogy, pp243-253, 2003.
 E.M. Fomitchev, “An Introduction to Wavelets and Wavelet Transform”, Systems and Programming Resources Inc, Tulsa, USA, 1998.
 C.M. Thoms, S.M. Dunn, C.F. Nodine, H.L. Kundel, "An analysis of Perceptual Errors in Reading Mammograms Using Quasi-local Spatial Frequency Spectra", Journal of Digit Imaging, Vol.4, pp117-123, 2001.
 Kaiser, “The Fast Haar Transform Potentials", IEEE Transaction, Volume 17, Issue 2 pp34-37, 1998.
 G. Keller and B. Warrack, “Statistical for Measurement and Economics”, Fifth Edition, 2001.