Automatic DWT2 thresholding based segmentation of the pigmented skin lesions in dermatoscopic images
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2014-11-01 https://doi.org/10.14419/ijet.v3i4.3536
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Abstract
The segmentation is the most important step to automatic diagnosis of the skin lesions. In this paper, a DWT2 thresholding based segmentation of dermatoscopic images has been proposed to diagnose of the pigmented skin lesions. In the proposed method, first, the image is converted to YUV channels and after denoising and contrast enhancement of the second channel of the converted image, it is decomposed to wavelet transform in two levels. Then, to more specificity and accuracy of segmentation, the Otsu’s thresholding method is applied on each sub-band of the second level of decomposed image and four thresholds are achieved. Subsequently, using adding all thresholds a new threshold is obtained and applied on the second level reconstructed image to achieve a binary image. Finally, post-processing is applied on this binary image using algorithms of morphological reconstructions, to increase the sensitivity. The experimental results show that the proposed method increases the accuracy to 90.97%, and specificity to 99.76%, compared with the other existing methods.
Keywords: DWT2, Morphological Reconstructions Algorithms, Otsu’s Thresholding, Pigmented Skin Lesions, Segmentation.
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References
- H. Sood, M. Shukla, “Various Techniques for Detecting Skin Lesion: A Review”, International Journal of Computer Science and Mobile Computing, Vol.3, Issue.5, pp. 905-912, May- 2014.
- S. P. B., S. Pande, “Segmentation of Melanoma Skin Cancer Images Based on Clustering Techniques”, Research Journal of Computer Systems Engineering-RJCSE, Vol.02, Issue. 05, October-December 2011.
- M. Zortea, S. O. Skrovseth, T. R. Schopf, H. M. Kirchesch, and F. Godtliebsen, “Automatic Segmentation of Dermoscopic Images by Iterative Classification”, International Journal of Biomedical Imaging, Vol. 2011, pp.1-19, 2011. http://dx.doi.org/10.1155/2011/972648.
- H. Talbot and L. Bischof, “An Overview of the Polartechnics SolarScan Melanoma Diagnosis Algorithms”, Proceedings of the APRS Workshop on Digital Image Computing, 2003, pp.33-38.
- J. J. P. Rajam, A. A. H. Thasneem, “Detection of Skin Lesions in Dermoscopic Images”, International Journal of Recent Development in Engineering and Technology, Vol.2, Special Issue 3, pp. 193-198, February 2014.
- F. Xie, A. C. Bovik, “Automatic Segmentation of Dermoscopy Images Using Self-generating Neural Networks Seeded by Genetic Algorithm”, Pattern Recognition, vol.46, pp.1012–1019, 2013. http://dx.doi.org/10.1016/j.patcog.2012.08.012.
- Md. A. H. Bhuiyan, I. Azad, Md. K. Uddin, “Image Processing for Skin Cancer Features Extraction”, International Journal of Scientific & Engineering Research, Vol.4, Issue 2, February-2013.
- M. Elgamal, “Automatic Skin Cancer Images Classification”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol.4, No. 3, 2013. http://dx.doi.org/10.14569/IJACSA.2013.040342.
- D. Ruiz, V. Berenguer, A. Soriano, B. Sánchez, “A Decision Support System for the Diagnosis of Melanoma: A Comparative Approach”, Expert Systems with Applications, Vol. 38, pp.15217–15223, 2011. http://dx.doi.org/10.1016/j.eswa.2011.05.079.
- J. Petrova, E. Hostalkova, “Edge Detection in Medical Image Using the Wavelet Transform”, Report of research, Department of Computing and Control Engineering, Czech Public, 2011.
- G. Nagendhar, D. Rajani, C. V. Sonagiri , V. Sridhar, “Text Localization in Video Data Using Discrete Wavelet Transform”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 1, Issue 2, December 2012.
- C. R. Jung “Combining Wavelets and Watersheds for Robust Multiscale Image Segmentation”, Image and Vision Computing, Vol.25, pp. 24–33, 2007. http://dx.doi.org/10.1016/j.imavis.2006.01.002.
- M. Khalili, D. Asatryan, “Colour Spaces Effects on Improved Discrete Wavelet Transform-based Digital Image Watermarking Using Arnold Transform Map”, IET Signal Process, Vol. 7, No. 3, pp. 1–11, 2013. http://dx.doi.org/10.1049/iet-spr.2012.0380.
- N. Kh. E. Abbadi and A. H. Miry, “Automatic Segmentation of Skin Lesions Using Histogram Thresholding”, Journal of Computer Science 10, Vol.4, pp.632-639, 2014.
- F. A. Tab, G. Naghdy and A. Mertins, “Scalable Multiresolution Color Image Segmentation”, Visual Communications and Image Processing, Vol.5960, 2005.
- N. M. Kwok, Q. P. Ha, G. Fang, “Effect of Color Space on Color Image Segmentation”, Image and Signal Processing. CISP '09. 2nd International Congress on, Tianjin, pp. 1 – 5, 2009.
- J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing”, IEEE Trans. Image Processing, Vol. 10, NO. 10, october 2001.
- H. Iyatomi, H. Oka, M. Saito, A. Miyake, M. Kimoto, J. Yamagami, S. Kobayashi, A. Tanikawa, M. Hagiwara, K. Ogawa, G. Argenziano, H. P. Soyer and M. Tanaka, “Quantitative Assessment of Tumour Extraction from Dermoscopy Images and Evaluation of Computer-based Extraction Methods for an Automatic Melanoma Diagnostic System”, Melanoma Research, Vol.16, pp. 183–190, 2006. http://dx.doi.org/10.1097/01.cmr.0000215041.76553.58.
- L. Fan, T. Gao, “A Novel Blind Robust Watermarking Scheme Based on Statistic Characteristic of Wavelet Domain Coefficients”, Proceedings of the International Conference on Signal Processing Systems, Tianjin, China, May 2009, pp. 121–125.
- H. Castillejos, V. Ponomaryov, L. N.-de-Rivera and V. Golikov, “Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation”, Computational and Mathematical Methods in Medicine, Vol. 2012, pp. 41–52, 2012. http://dx.doi.org/10.1155/2012/578721.
- J. Glaister, A. Wong, and D. A. Clausi, “Segmentation of Skin Lesions from Digital Images Using Joint Statistical Texture Distinctiveness”, IEEE Trans. Biomedical Engineering. Vol. 61, Issue. 4, pp.1220 – 1230, 2014. http://dx.doi.org/10.1109/TBME.2013.2297622.
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How to Cite
Razazzadeh, N., & Khalili, M. (2014). Automatic DWT2 thresholding based segmentation of the pigmented skin lesions in dermatoscopic images. International Journal of Engineering & Technology, 3(4), 529-534. https://doi.org/10.14419/ijet.v3i4.3536Received date: 2014-09-03
Accepted date: 2014-09-28
Published date: 2014-11-01