Abstract
Detection of new or rapidly evolving melanocytic lesions is crucial for early diagnosis and treatment of melanoma. We propose a fully automated pre-screening system for detecting new lesions or changes in existing ones, on the order of 2 − 3mm, over almost the entire body surface. Our solution is based on a multi-camera 3D stereo system. The system captures 3D textured scans of a subject at different times and then brings these scans into correspondence by aligning them with a learned, parametric, non-rigid 3D body model. This means that captured skin textures are in accurate alignment across scans, facilitating the detection of new or changing lesions. The integration of lesion segmentation with a deformable 3D body model is a key contribution that makes our approach robust to changes in illumination and subject pose.
References
Bogo, F., Romero, J., Loper, M., Black, M.J.: FAUST: Dataset and evaluation for 3D mesh registration. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2014)
Dunki-Jacobs, E., Callender, G., McMasters, K.: Current management of melanoma. Current Problems in Surgery 50, 351–382 (2013)
Huang, H., Bergstresser, P.: A new hybrid technique for dermatological image registration. In: IEEE International Conference on BioInformatics and BioEngineering (BIBE), pp. 1163–1167 (2007)
Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: A review. Artificial Intelligence in Medicine 56(2), 69–90 (2012)
Mirzaalian, H., Hamarneh, G., Lee, T.: A graph-based approach to skin mole matching incorporating template-normalized coordinates. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2152–2159 (2009)
Mirzaalian, H., Lee, T., Hamarneh, G.: 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.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 98–105. Springer, Heidelberg (2012)
Perednia, D., White, R., Schowengerdt, R.: Automated feature detection in digital images of skin. Computer Methods and Programs in Biomedicine 34, 41–60 (1991)
Perednia, D., White, R., Schowengerdt, R.: Automatic registration of multiple skin lesions by use of point pattern matching. Computerized Medical Imaging and Graphics 16, 205–216 (1991)
Pierrard, J., Vetter, T.: Skin detail analysis for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)
Taeg, S., Freeman, W., Tsao, H.: A reliable skin mole localization scheme. In: IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)
Voigt, H., Classen, R.: Topodermatographic image analysis for melanoma screening and the quantitative assessment of tumor dimension parameters of the skin. Cancer 75, 981–988 (1995)
Weiss, A., Hirshberg, D., Black, M.J.: Home 3D body scans from noisy image and range data. In: IEEE International Conference on Computer Vision (ICCV), pp. 1951–1958 (2011)
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Bogo, F., Romero, J., Peserico, E., Black, M.J. (2014). Automated Detection of New or Evolving Melanocytic Lesions Using a 3D Body Model. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_74
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DOI: https://doi.org/10.1007/978-3-319-10404-1_74
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