Segmentation of Melanoma and Other Pigmented Skin Lesions in Dermoscopic Images Using Fusion of Threshoding Methods based on Reinforcement Algorithm

Subject Areas : Renewable energy

Seyyed Mohammad Seyyed Ebrahimi ¹ (MSc /Islamic Azad University, Najafabad Branch)
Hossein Pourghasem ² (Assistant Professor /Islamic Azad University ,Najafabad Branch)
Ahmad Keshavarz ³ (Assistant Professor /Persian Gulf University, Boushehr)

Received: 2012-12-10 Accepted : 2013-10-08 Published : 2014-02-01

Keywords: detection, Thresholding, Dermoscopy Images, reinforcement algorithm, Melanoma,

Abstract :

Dermoscopy is one of the major imaging techniques used in diagnoses of Melanoma and other skin diseases. Because of difficulties and subjectivity of human interpretation, automatic and computerized analysis of dermoscopic images has opened an important research area. Automatic lesion detection is one of the main steps in analysis of these images. Finding an optimal threshold for segmenting the lesion is a severe task in image processing. Different methods for thresholding already exist. In this research a novel thresholding approach according to well-known thresholding methods and reinforcement algorithm for segmenting dermoscopic images is presented. The reinforced agent learns optimal weights for different thresholding methods and finally segments the dermoscopic image with optimal threshold. A reward function is designed for achieving the similarity ratio between the binary output image and original gray level image and calculating reward/punish signal which should be exerted to reinforced agent. We use three thresholding methods, Otsu, Kittler and Kapur, for combining in the reinforced agent and the detected lesions are compared with the ground-truth which is determined dermatologists and the border error is calculated. The results are also compared with other well-known automatic methods which indicate that the proposed method yields to more accuracy and less border error in detection of lesion in dermocopy images.

References:

[1] A. Jemal, R. Sigel, E. Ward, Y. Hao, J. Xu, M.J. Thum, "Cancer Statistics 2009", CA Cancer J. Clin, Vol. 59, pp. 225-249, 2009.

[2] G. Argenziano, H.P. Soyer, V.D. Giorgi, "Dermoscopy: A tutorial", EDRA Medical publishing & new media, Milan, Italy, 2002.

[3] K. Steiner, M. Schemper, "Statistical evaluation of epiluminescence dermoscopy criteria for melanocytic pigmented lesions", J. Am. Acad. Dermatol., Vol. 29, No. 4, pp. 581-588, 1993.

[4] W.V. Stoecker, K. Gupta, R.J. Stanley, R. Joe, R.H. Moss, Bijaya, Shrestha, "Detection of asymmetric blotches in dermoscopy images of malignant melanoma using relative color", Skin Res. Technol., Vol. 11, No. 3, pp. 179-184, 2005.

[5] M.E. Celebi, H.A. Kingravi, Y.A. Aslandogan, W.V. Stoecker, "Detection of blue-white veil areas in dermoscopy images using machine learning techniques", Proc. of SPIE Medical Imaging Conf., SanDiego, pp. 1861-1868, 2006.

[6] M.E. Celebi, H. Iyatomi, G. Schaefer, W.V. Stoecker, "Lesion border detection in dermoscopy images", Computerized Medical Imaging and Graphics, Vol. 33, No. 2, pp. 148-153, 2009.

[7] G. Rahil, A. Mohammad, "Skin lesion segmentation using color channel optimization and clustering-based histogram thresholding", International Journal of Medicine and Medical Sciences, Vol. 1,pp. 126-133, 2010.

[8] M. Celebi, Y. Aslandogan, "Unsupervised border detection in dermoscopy images", Skin Research and Technology, Vol. 1, pp. 1-9, 2007.

[9] 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, S.W. Menzies, "Border detection in dermoscopy images using statistical region merging", Skin Research and Technology, Vol. 14, pp. 347-353, 2008.

[10] M. Celebi, K. Hassan, H. Iyatomi, "Fast and accurate border detection in dermoscopy images using statistical region merging", Skin reseach and technology, Vol. 14, pp.1-7, 2007.

[11] J. Tang, "A multi-direction Gvf snake for the segmentation of skin cancer images", Pattern Recognition, Vol. 42, pp. 1172-1179, 2009.

[12] S.G. Rajab, "Skin lesion segmentation using Co-operative neural network edge detection and color normalization", 9th IEEE international conference on Biomedicine, pp. 1-4, 2009.

[13] Y. Borlu, "Accurate segmentation of Dermoscopic images by image thresholding based on Type-2 fuzzy logic", IEEE Transactions on Fuzzy Systems, Vol. 17, No. 4, pp. 976-982, 2009.

[14] B. Sankur, M. Sezgin, "Survey over image thresholding techniques and quantitative performance evaluation", J. Electron. Imaging, Vol. 13, No.1, pp. 146–165, 2004.

[15] R.S. Sutton, A.G. Barto, Reinforcement Learning: An introduction, Cambridge, MIT press, 1998.

[16] F. Melgani, "Robust image binarization with ensembles of thresholding algorithms", J. Electron Imaging, Vol. 15, pp. 023010, 2006.

[17] B. Sankur, M. Sezgin, "Survey over image thresholding techniques and quantitative performance evaluation", J. Electron. Imaging, Vol. 13, No. 1, pp. 146–165, 2004.

[18] J. Kittler, J. Illingworth, "Minimum error thresholding", Pattern Recognition, Vol. 19, No. 1, pp. 41-47, 1986.

[19] J.N. Kapur, P.K. Sahoo, A.K.C. Wong, "A new method for gray-level picture thresholding using the entropy of the histogram", Graph Model Im. Proc, Vol. 29, pp. 273–285, 1985.

[20] M. Shokri, H.R. Tizhoosh, "A reinforcement agent for threshold fusion", Applied Soft Computing, Vol. 8, pp. 174-181, 2008.

[21] G. Argenziano, H.P. Soyer, D.G. Vet, Dermoscopy:a tutorial. Milan, Italy: EDRA Medical Publishing & New Media, 2002.

[22] P. Pagadala, Tumor border detection in epiluminescence microscopy images. MS Thesis. Department of Electricaland Computer Engineering, University of Missouri- Rolla, 1998.

[23] B. Erkol, R.H. Moss, R.J. Stanley, W.V. Stoecker, E. Hvatum, "Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes", Skin Res Technol, Vol. 11, pp.17-26, 2005.

[24] G. Argenziano, H.P. Soyer, and V. De Giorgi et al., Dermoscopy:A Tutorial, EDRA Medical Publishing &New Media, 2002.

[25] 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, S.W. Menzies, "Border detection in dermoscopy images using statistical region merging", Skin Research and Technology,Vol. 14, pp. 347-353, 2008.

[26] R.C. Gonzalez, R.E. Woods, Digital Image Processing, 2nd ed., Prentice Hall, New Jersey, 2002, 07458.