Abstract
Segmentation of skin lesion is an important step in the overall automated diagnostic systems used for early detection of skin cancer. Skin lesions can have various different forms which makes segmentation a difficult and complex task. Different methods are present in literature for improving results for skin lesion segmentation. Each method has some pros and cons and it is observed that none of them can be regarded as a generalized method working for all types of skin lesions. The paper proposes an algorithm that combines the advantages of clustering, thresholding and active contour methods currently being used independently for segmentation purposes. A modified algorithm for thresholding based on fusion of Fuzzy C mean clustering and histogram thresholding is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. The performance of level set segmentation is subject to appropriate initialization, so the proposed algorithm is being compared with some other state-of-the-art initialization methods. The work has been tested on clinical database of 270 images. Parameters for performance evaluation are presented in detail. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of the proposed method for skin cancer detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Siegel, R., et al.: Cancer statistics. CA: A Cancer Journal for Clinicians 61(4), 212–236 (2011)
Society, A.C., Cancer Facts & Figures (2012), http://www.cancer.org/acs/groups/content/epidemiologysurveilance/documents/document/acspc-031941.pdf
Piccolo, D., et al.: Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study. British Journal of Dermatology 147, 481–486 (2002)
Ben Chaabane, S., et al.: Color image segmentation using automatic thresholding and the fuzzy C-means techniques. In: Proceedings of the 14th IEEE Mediterranean Electro technical Conference, pp. 857–861 (2008)
Dongju, L., Jian, Y.: Otsu Method and K-means. In: Proceedings of the Ninth International Conference on Hybrid Intelligent Systems, China, pp. 344–349 (2009)
Emre Celebi, M., et al.: Border detection in dermoscopy images using statistical region merging. Skin Research and Technology 14, 347–353 (2008)
Emre Celebi, M., Schaefer, G., Iyatomi, H., Stoecker, W.V.: Lesion Border Detection in Dermoscopy Images. Computerized Medical Imaging & Graphics 33, 148–153 (2009)
Silveira, M., et al.: Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images. IEEE Journal of Selected Topics in Signal Processing 3, 35–45 (2009)
Blake, A., Isard, M.: Active Contours. Springer (1998)
Erkol, B., Moss, R.H., Stanley, R.J., Stoecker, W.V., Hvatum, E.: Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Research and Technology 11, 17–26 (2005)
Nascimento, J.C., et al.: Adaptive snakes using the EM algorithm. IEEE Transactions on Image Processing 14, 1678–1686 (2005)
Li, B.N., et al.: Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine 41, 1–10 (2011)
Abbas, Q., Fondón, I., Rashid, M.: Unsupervised skin lesions border detection via two-dimensional image analysis. Computer Methods and Programs in Biomedicine 104, 1–15 (2011)
Aja-Fernandez, et al.: Soft thresholding for medical image segmentation. In: Proceedings 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Argentina, pp. 4752–4755 (2010)
Jun, Z., Jinglu, H.: Image Segmentation Based on 2D Otsu Method with Histogram Analysis. In: Proceedings 2008 International Conference on Computer Science and Software Engineering, pp. 105–108 (2008)
Tobias, O.J., Seara, R.: Image segmentation by histogram thresholding using fuzzy sets. IEEE Transactions on Image Processing 11, 1457–1465 (2002)
Sookpotharom, S.: Border Detection of Skin Lesion Images Based on Fuzzy C-Means Thresholding. In: Proceedings of 3rd Int. Conference on Genetic and Evolutionary Computing, China, pp. 777–780 (2009)
Chunming, L., et al.: Level set evolution without re-initialization: A new variational formulation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, pp. 430–436 (2005)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 2, 266–277 (2001)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. International Journal of Computer Vision 22, 61–79 (1997)
Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, New York (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Masood, A., Al-Jumaily, A.A., Maali, Y. (2013). Level Set Initialization Based on Modified Fuzzy C Means Thresholding for Automated Segmentation of Skin Lesions. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)