IJMTES – AUTOMATED SEGMENTATION OF MICROSCOPY IMAGED MELANOCYTIC LESIONS AND CLASSIFICATION

Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : P Kavipriya | Prof S Pandiarajan

Volume 02 Issue 07  Year 2015

ISSN no: 2348-3121

Page no: 4-6

Abstract The incidence rate of cutaneous malignant melanoma, a type of skin cancer developing from melanocytic skin lesions, has risen to alarmingly high levels. As there is no effective treatment for advanced melanoma, recognizing the lesion at an early stage is crucial for successful treatment. Algorithm for unsupervised skin-lesion segmentation and the necessary pre-processing. Starting with a digital dermoscopic image of a lesion surrounded by healthy skin, the pre-processing steps are noise filtering, illumination correction and removal of artifacts. A median filter is used for noise removal, because of its edge-preserving capabilities and computer efficiency. A new, robust and computer effective algorithm for hair removal, based on morphological operations of binary images. The segmentation algorithm is based on global thresholding and histogram analysis. Unlike most segmentation algorithms based on histogram analysis. Optimal thresholding typically takes a grayscale or color image as input and, in the simplest implementation, outputs a binary image representing the segmentation. For each pixel in the image, a threshold has to be calculated. If the pixel value is below the threshold it is set to the background value, otherwise it assumes the foreground value. Classify the input image like melanoma, carcinoma, keratosis skin cancer using support vector machine.

Keywords— Malignant, Melanoma, Dermatoscopy, Carcinoma

Reference

[1] D. S. Rigel, R. J. Friedman, and A. W. Kopf, “The incidence of malignant melanoma in the United States: Issues as we approach the 21st century,”J. Amer. Acad. Dermatol., Vol. 34, No. 5, pp. 839–847, (1996).
[2]Nachbar, W. Stolz, T. Merkle, A. Cognetta, T. Vogt, M. Landthaler, P. Bilek, O. Braun-Falco, and G. Plewig, “The ABCD rule of dermatoscopy,”J. Amer. Acad. Dermatol., Vol. 30, No. 4, pp. 551–559, (1994).
[3] J. Gao, J. Zhang, M. G. Fleming, I. Pollak, and A. B. Cognetta, “Segmentation of dermatoscopic images by stabilized inverse diffusion equations, “in Proc. Int. Conf. Image Process., 1998, pp. 823–827.
[4] M. E. Celebi, H. A. Kingravi, H. Iyatomi, Y. A. Aslandogan,W. V.Stoecker, R. H. Moss, J. M. Malters, J. M. Grichnik,A. A. Marghoob, H. S. Rabinovitz, and S. W. Menzies, Border detection in dermoscopy images using statistical region merging,” Skin Res.Tech. vol. 14, no. 3, pp. 347– 353, (2008).
[5] Peruch, F. Bogo, M. Bonazza, M. Bressan, V. Cappelleri, andE. Peserico, “Simple, fast, accurate melanocytic lesion segmentation in 1D colour space,” in Proc. Proc. Int. Conf. Comput. Vision Theory Appl., (2013), pp. 191–2000.
[6]K. Korotkov and R. Garcia, “Computerized analysis of pigmented skin lesions: A review,” Artif. Intell. Med., Vol. 56, No. 2, pp. 69–90, 2012.
[7]M. E. Celebi, Q. Wen, S. Hwang, H. Iyatomi, and G. Schaefer, “Lesionborder detection in dermoscopy images using ensembles of thresholding methods,” Skin Res. Tech., Vol. 19, No. 1, pp. e252–e258, (2013).
[8] H. Zhou, G. Schaefer, M. E. Celebi, F. Lin, and T. Liu, “Gradient vector flow with mean shift for skin lesion segmentation,” Comput. Med. Imag Graph., Vol. 35, No. 2, pp. 121–127, (2011).
[9] M. Fiorese, E. Peserico, and A. Silletti, “VirtualShave: Automated hair removal from digital dermatoscopic images,” in Proc. IEEE Eng. Med. Biol. Soc., (2011), pp. 5145–5148.
[10] H. Abdi and L. J. Williams, “Principal component analysis,” WIREsComp. Stat., Vol. 2, No. 4, pp. 433–459, 201.

Full Paper: Click Here

 

Scroll Up