Analysis of Skin Cancer Using Fuzzy and Wavelet Technique – Review & Proposed New Algorithm

Analysis of Skin Cancer Using Fuzzy and Wavelet Technique – Review & Proposed New Algorithm

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-4 Issue-6                      
Year of Publication : 2013
Authors : Nilkamal S. Ramteke , Shweta V.Jain

Citation 

Nilkamal S. Ramteke , Shweta V.Jain."Analysis of Skin Cancer Using Fuzzy and Wavelet Technique – Review & Proposed New Algorithm". International Journal of Engineering Trends and Technology (IJETT). V4(6):2555-2566 Jun 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.

Abstract

This paper first reviews the past and present technologies for skin cancer detections along with their relevant tools. Then it goes on discussing briefly abou t features, advantages or drawbacks of each of them. Then we discuss the mathematics preliminary required to process the image of skin cancer lesion using our proposed scheme. This paper presents a new approach for Skin Cancer detection and analysis from g iven photograph of patient’s cancer affected area, which can be used to automate the diagnosis and theruptic treatment of skin cancer. The proposed scheme is using Wavelet Transformation for image improvement, denoising and Histogram Analysis whereas ABCD rule with good diagnostic accuracy worldwide is used in diagnostic system as a base and finally Fuzzy Inference System for Final decision of skin type based on the pixel color severity for final decision of Benign or Malignant Skin Cancer.

References

[1] www.s kincancer.org
[2] Indira, D.N.V.S.L.S. and Jyotsna Suprya, P., “Detection & Analysis of Skin Cancer using Wavelet Techniques,” International Journal of Computer Science and Information Technologies, Vol. 2(5), pp.1927 - 1932, 2011.
[3] Jain, Y. K. and Jain, M., “Com parison between Different Classification Methods with Application to Skin Cancer,” International Journal of Computer Applications, Vol. 53, No.11, pp. 18 - 24 , Sept. 2012 .
[4] Fatima, R., Khan, Mohd. Z. A., Govardhan, A. and Kashyap, D. D., “Computer Aided Multi - Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma,” International Journal of Computer Science and Network Security, Vol.12, No.10, pp.74 - 86 , Oct. 2012 .
[5] Alamelumangai, N. and DeviShree. J., “PSO Aided Neuro Fuzzy Inference System f or Ultrasound Image Segmentation,” International Journal of Computer Appl ications, Vol. 7, No.14 , pp. 16 - 20 , Oct. 2010 .
[6] Fassihi, N., Shanbehzadeh, J., Sarafzadeh, A., and Ghasemi, E., “Melanoma Diagnosis by the Use of Wavelet Analysis based on Morphologi cal Operators,” Proc. of International MultiConference of Engineers and Computer Scientists 2011, Vol. I, Hong Kong, pp. 1 - 4 , March 16 - 18, 2011 .
[7] Alcon, J. F., Ciuhu, C., Kate, W. ten, Heinrich, A., Uzunbajakava, N., Krekels, G., Siem, D. and Haan, G. de., “Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis,” IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 1, Feb. 2009, pp. 14 - 25.
[8] Jung, C. R., and Scharcanski, J., “Sharpening Der matological Color Images in the Wavelet Domain,” IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 1 , pp.4 - 13 , Feb. 2009 .
[9] Odeh, S. M.,“Using an Adaptive Neuro - Fuzzy Inference System (AnFis) Algorithm for Automatic Diagnosis of Skin Cancer,” Journal of Communication and Computer,Vol.8, pp.751 - 755 , 2011 .
[10] Sigurdsson, S., Philipsen, P. A., Hansen, L. K., Larsen, J., Gnidecka, M. and Wulf, H. C., “Detection of Skin Cancer by Classification of Raman Spectra,” IEEE Trans. on Biomedical Engineering, Vol.51, No.10, pp.1784 - 1793, Oct. 2004.
[11] Bhattacharyya, S., “A Brief Survey of Color Image Preprocessing and Segmentation Techniques,” Journal of Pattern Recognition Research, Vol.1, pp. 120 - 129 , 2011 .
[12] Ogorza?ek, M. J., Surowak, G., Nowak, L. and Merkwirth, C.., “New Approaches for Computer - Assisted Skin Cancer Diagnosis,” The Third International Symposium on Optimization and Systems Biology, Zhangjiajie, China, Sept. 20 - 22, pp. 65 - 72 , 2009 .
[13] Blackledge , J. M. and Dubovitskiy, D. A., “Object Detection and Classification with Applications to Skin Cancer Screening,” ISAST Transactions on Intelligent Systems, Vol. 1, No. 2, pp.34 - 45 , 2008 .
[14] Silveira, M., Nascimento, J. C., Marques, J. S., Marcel, A. R. S., Mendonça, T., Yamauchi, S., Maeda, J. and Rozeira, J., “Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images,” IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 1, pp.35 - 45 , Feb. 2009 .
[15] Patwardhan, S. V., Dhawan, A. P. and Relue, P. A., “Classification of melanoma using tree structured wavelet Transforms,” Computer Methods and Programs in Biomedicine, Vol. 72, pp.223 - 239, 2003.
[16] Day, G. R. and Barbour, R. H., “Automated melanoma diagnosis: where are we at?,” Skin Re search and Technology, pp.1 - 5 , 2000 .
[17] www.emedicinehealth.com
[18] Rigel, D.S., Russak, J. and Friedman, R., “The Evolution of Melanoma Diagnosis: 25 Years Beyond the ABCDs,” CA Cancer Journal of Clinicians, No. 60, pp. 301 - 316, 2010.
[19] Ercal, F., Chawla, A., Stoe cker, W.V., Lee, H - C. and Moss, R.H., “Neural network diagnosis of malignant melanoma from color images,” IEEE Trans. of Biomedical Engineering, Vol.14, No.9, pp.837 - 845, Sep t . 1994.
[20] Herbin, M., Bon, F., Venot, A., Jeanlouis, F., Dubertret, M., Dubertret, L. and Stauch, G., “Assessment of healing kinetics through the true color image processing,” IEEE Trans. of Medical Imaging, Vol.12, No.1, pp.39 - 43, May 1993.
[21] Sanders, J., Goldstein, B., Leotta, D. and Richards, K., “Image processing techniques for quantit ative analysis of skin structures,” Computational Methods and Programming in Biomedical, Vol.59, pp.167 - 180, 1999.
[22] Ganster, H., Pinz, P., Rohrer, R., Wildling, E., Binder, M., Kittler, H., “Automated melanoma recognition,” IEEE Trans. of Medical Imaging, V ol.20, No.3, pp.233 - 239, Mar. 2001.
[23] Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S., “A new algorithms for border description of polarized light surface microscopic images of pigmented skin lesions,” IEEE Trans. of Medical Imaging, Vol.22, No.8, pp.959 - 964, Aug. 2003.
[24] Chwirot, B. W., Chwirot, S., Redziski, J. and Michniewicz, Z., “Detection of melanomas by digital imaging of spectrally resolved ultraviolet light - induced autofluorescence of human skin,” European Journal of Cancer, Vol.34, pp.1730 - 1734, Oct. 1998.
[25] Aberg, P., Nicander, I. , Hansson, J., Geladi, P., Homgren, U., and Ollmar, S., “Skin cancer identification using multifrequency electrical impedance – A potential screening tool,” IEEE Trans. of Biomedical Engineering, Vol.51, No.12, pp.2097 - 2102, Dec. 2004.
[26] Pehamberger, H., Ste iner, A., Wolff, K., “In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions,” Journal American Academy of Dermatology, No. 17, pp.571 - 583, 1987.
[27] Stolz, W., Riemann, A., Cognetta, A.B., Pillet, L., Abmay r, W., Holzel, D., “ABCD rule of Dermatoscopy: a new practical method for early recognition of malignant melanoma,” European Journal of Dermatology, No.4, pp.521 - 527, 1994.
[28] www.metrohealth.org
[29] Argenziano, G., Fabbrocini, G., Carli, P., De Giorgi, V., Samma rco, E, Delfino, M., “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,” Arch. Dermatology, No.134, pp. 1563 - 1570, 1988.
[30] Hen ning, J. S., Dusza, S. W., Wang, S. Q., “The CASH (color, architecture, symmetry and homogeneity) algorithm for dermoscopy,” Journal of American Academy of Dermatology, No. 56, pp. 45 - 52, 2007.
[31] Menzies, S.W., Crotty, K.A., Ingvar, C., McCarthy W.H., “An at las of surface microscopy of pigmented skin lesions,” McGraw - Hill Book Company, Sydney, 2003.
[32] Annessi, G., Bono, R., Sampogna, F., Faraggiana, T., Abeni, D., “Sensitivity, specificity and diagnostic accuracy of three dermoscopic algorithmic methods in the diagnosis of doubtful melanocytic lesions: the importance of light brown structureless areas in differentiating atypical melanocytic nevi from thin melanomas,” Journal of American Academy of Dermatology, No. 56, pp. 759 - 767, 2007.

Keywords
Skin Cancer, Melanoma, Fuzzy Inference System, Wavelet, Segmentation .