Abstract
We formulate the pigmented-skin-lesion (PSL) matching problem as a relaxed labeling of an association graph. In this graph labeling problem, each node represents a mapping between a PSL from one image to a PSL in the second image and the optimal labels are those optimizing a high order Markov Random Field energy (MRF). The energy is made up of unary, binary, and ternary energy terms capturing the likelihood of matching between the points, edges, and cliques of two graphs representing the spatial distribution of the two PSL sets. Following an exploration of various MRF energy terms, we propose a novel entropy energy term encouraging solutions with low uncertainty. By interpreting the relaxed labeling as a measure of confidence, we further leverage the high confidence matching to sequentially constrain the learnt objective function defined on the association graph. We evaluate our method on a large set of synthetic data as well as 56 pairs of real dermatological images. Our proposed method compares favorably with the state-of-the-art.
Chapter PDF
Similar content being viewed by others
Keywords
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
Duchenne, O., Bach, F., Kweon, I., Ponce, J.: A tensor-based algorithm for high-order graph matching. IEEE TPAMI 1(99), 1–13 (2011)
Gallagher, R., Rivers, J., Lee, T., Bajdik, C., McLean, D.I., Coldman, A.: Broad-Spectrum Sunscreen Use and the Development of New Nevi in White Children: A Randomized Controlled Trial. JAMA 283(22), 2955–2960 (2000)
Huang, H., Bergstresser, P.: A new hybrid technique for dermatological image registration. IEEE BIBE, 1163–1167 (2007)
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)
Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: IEEE ICCV, vol. 2, pp. 1482–1489 (2005)
Leordeanu, M., Zanfir, A., Sminchisescu, C.: Semi-supervised Learning and Optimization for Hypergraph Matching. In: IEEE ICCV (2011)
Mirzaalian, H., Hamarneh, G., Lee, T.: Graph-based approach to skin mole matching incorporating template-normalized coordinates. In: IEEE CVPR, pp. 2152–2159 (2009)
Perednia, D.A., White, R.G.: Automatic registration of multiple skin lesions by use of point pattern matching. CMIG 16(3), 205–216 (1992)
Roning, J., Riech, M.: Registration of nevi in successive skin images for early detection of melanoma. In: IEEE ICPR, vol. 1, pp. 352–357 (1998)
Torresani, L., Kolmogorov, V., Rother, C.: Feature Correspondence Via Graph Matching: Models and Global Optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008)
Zass, R., Shashua, A.: Probabilistic graph and hypergraph matching. In: IEEE CVPR, pp. 1–8 (2008)
Zeng, Y., Wang, C., Yang, W., Gu, X., Samaras, D., Paragios, N.: Dense non-rigid surface registration using high-order graph matching. In: IEEE CVPR, pp. 382–389 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mirzaalian, H., Lee, T.K., Hamarneh, G. (2012). Uncertainty-Based Feature Learning for Skin Lesion Matching Using a High Order MRF Optimization Framework. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-33418-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33417-7
Online ISBN: 978-3-642-33418-4
eBook Packages: Computer ScienceComputer Science (R0)