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Conference Paper

Automated Detection of New or Evolving Melanocytic Lesions Using a 3D Body Model

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons85176

Bogo,  Federica
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons85106

Romero,  Javier
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75293

Black,  Michael J.
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Bogo, F., Romero, J., Peserico, E., & Black, M. J. (2014). Automated Detection of New or Evolving Melanocytic Lesions Using a 3D Body Model. In P. Golland, N. Hata, C. Barillot, J. Hornegger, & R. Howe (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014. Proceedings, Part I (pp. 593-600). Cham et al.: Springer International Publishing. doi:10.1007/978-3-319-10404-1_74.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-E367-2
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.