Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

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

MPG-Autoren
/persons/resource/persons85176

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

/persons/resource/persons85106

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

/persons/resource/persons75293

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

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0024-E367-2
Zusammenfassung
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.