Abstract
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In this article, we present an approximate lesion localization method that serves as a preprocessing step for detecting borders in dermoscopy images. In this method, first the black frame around the image is removed using an iterative algorithm. The approximate location of the lesion is then determined using an ensemble of thresholding algorithms. Experiments on a large set of images demonstrate that the presented method achieves both fast and accurate localization of lesions in dermoscopy images.
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Keywords
- Markov Random Field
- Thresholding Method
- Thresholding Algorithm
- Computerize Medical Imaging
- Border Detection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Jemal, A., Siegel, R., Ward, E., et al.: Cancer Statistics. CA: A Cancer Journal for Clinicians 2008 58(2), 71–96 (2008)
Argenziano, G., Soyer, H.P., De Giorgi, V., et al.: Dermoscopy: A Tutorial. EDRA Medical Publishing & New Media, Milan (2002)
Steiner, K., Binder, M., Schemper, M., et al.: Statistical Evaluation of Epiluminescence Dermoscopy Criteria for Melanocytic Pigmented Lesions. Journal of American Academy of Dermatology 29(4), 581–588 (1993)
Binder, M., Schwarz, M., Winkler, A., et al.: Epiluminescence Microscopy. A Useful Tool for the Diagnosis of Pigmented Skin Lesions for Formally Trained Dermatologists. Archives of Dermatology 131(3), 286–291 (1995)
Fleming, M.G., Steger, C., Zhang, J., et al.: Techniques for a Structural Analysis of Dermatoscopic Imagery. Computerized Medical Imaging and Graphics 22(5), 375–389 (1998)
Celebi, M.E., Kingravi, H.A., Uddin, B., et al.: A Methodological Approach to the Classification of Dermoscopy Images. Computerized Medical Imaging and Graphics 31(6), 362–373 (2007)
Iyatomi, H., Oka, H., Saito, M., et al.: Quantitative Assessment of Tumor Extraction from Dermoscopy Images and Evaluation of Computer-based Extraction Methods for Automatic Melanoma Diagnostic System. Melanoma Research 16(2), 183–190 (2006)
Celebi, M.E., Aslandogan, Y.A., Stoecker, W.V., et al.: Unsupervised Border Detection in Dermoscopy Images. Skin Research and Technology 13(4), 454–462 (2007)
Celebi, M.E., Kingravi, H.A., Iyatomi, H., et al.: Border Detection in Dermoscopy Images Using Statistical Region Merging. Skin Research and Technology 14(3), 347–353 (2008)
Lee, T.K., Ng, V., Gallagher, R., et al.: Dullrazor: A Software Approach to Hair Removal from Images. Computers in Biology and Medicine 27(6), 533–543 (1997)
Stoecker, W.V., Gupta, K., Stanley, R.J., et al.: Detection of Asymmetric Blotches in Dermoscopy Images of Malignant Melanoma Using Relative Color. Skin Research and Technology 11(3), 179–184 (2005)
Celebi, M.E., Iyatomi, H., Stoecker, W.V., et al.: Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images. Computerized Medical Imaging and Graphics 32(8) (to appear, 2008)
Melgani, F.: Robust Image Binarization with Ensembles of Thresholding Algorithms. Journal of Electronic Imaging 15(2), 023010, 11 pages (2006)
Otsu, N.: A Threshold Selection Method from Gray Level Histograms. IEEE Trans. on Systems, Man and Cybernetics 9(1), 62–66 (1979)
Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram. Graphical Models and Image Processing 29(3), 273–285 (1985)
Huang, L.-K., Wang, M.-J.J.: Image Thresholding by Minimizing the Measures of Fuzziness. Pattern Recognition 28(1), 41–51 (1995)
Yen, J.C., Chang, F.J., Chang, S.: A New Criterion for Automatic Multilevel Thresholding. IEEE Trans. on Image Processing 4(3), 370–378 (1995)
Sahoo, P.K., Wilkins, C., Yeager, J.: Threshold Selection Using Renyi’s Entropy. Pattern Recognition 30(1), 71–84 (1997)
Li, C.H., Tam, P.K.S.: An Iterative Algorithm for Minimum Cross Entropy Thresholding. Pattern Recognition Letters 18(8), 771–776 (1998)
Hance, G.A., Umbaugh, S.E., Moss, R.H., Stoecker, W.V.: Unsupervised Color Image Segmentation with Application to Skin Tumor Borders. IEEE Engineering in Medicine and Biology 15(1), 104–111 (1996)
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Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V. (2009). Localization of Lesions in Dermoscopy Images Using Ensembles of Thresholding Methods. In: Wada, T., Huang, F., Lin, S. (eds) Advances in Image and Video Technology. PSIVT 2009. Lecture Notes in Computer Science, vol 5414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92957-4_95
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DOI: https://doi.org/10.1007/978-3-540-92957-4_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92956-7
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