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Perceiving Systems Conference Paper 2014

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

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Perceiving Systems
Federica Bogo
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Perceiving Systems
Javier Romero
Affiliated Researcher
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Perceiving Systems
Michael Black
Director
Miccai

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 diff erent 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.

Author(s): Federica Bogo and Javier Romero and Enoch Peserico and Michael J. Black
Book Title: Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Volume: 8673
Pages: 593--600
Year: 2014
Month: September
Series: Lecture Notes in Computer Science
Editors: Golland, Polina and Hata, Nobuhiko and Barillot, Christian and Hornegger, Joachim and Howe, Robert
Publisher: Spring International Publishing
Project(s):
Bibtex Type: Conference Paper (inproceedings)
DOI: 10.1007/978-3-319-10404-1_74
Event Name: Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Event Place: Boston, MA, USA
Electronic Archiving: grant_archive
Links:

BibTex 

@inproceedings{bogo:miccai:2014,
  title = {Automated Detection of New or Evolving Melanocytic Lesions Using a {3D} Body Model},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  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.},
  volume = {8673},
  pages = {593--600},
  series = {Lecture Notes in Computer Science},
  editors = {Golland, Polina and Hata, Nobuhiko and Barillot, Christian and Hornegger, Joachim and Howe, Robert},
  publisher = {Spring International Publishing},
  month = sep,
  year = {2014},
  slug = {bogo-miccai-2014},
  author = {Bogo, Federica and Romero, Javier and Peserico, Enoch and Black, Michael J.},
  month_numeric = {9}
}