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Detecting Acromegaly: Screening for Disease with a Morphable Model

  • Erik Learned-Miller
  • Qifeng Lu
  • Angela Paisley
  • Peter Trainer
  • Volker Blanz
  • Katrin Dedden
  • Ralph Miller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)

Abstract

Acromegaly is a rare disorder which affects about 50 of every million people. The disease typically causes swelling of the hands, feet, and face, and eventually permanent changes to areas such as the jaw, brow ridge, and cheek bones. The disease is often missed by physicians and progresses beyond where it might if it were identified and treated earlier. We consider a semi-automated approach to detecting acromegaly, using a novel combination of support vector machines (SVMs) and a morphable model. Our training set consists of 24 frontal photographs of acromegalic patients and 25 of disease-free subjects. We modelled each subject’s face in an analysis-by-synthesis loop using the three-dimensional morphable face model of Blanz and Vetter. The model parameters capture many features of the 3D shape of the subject’s head from just a single photograph, and are used directly for classification. We report encouraging results of a classifier built from the training set of real human subjects.

Keywords

Support Vector Machine Acromegalic Patient Shape Vector Morphable Model Brow Ridge 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Erik Learned-Miller
    • 1
  • Qifeng Lu
    • 1
  • Angela Paisley
    • 2
  • Peter Trainer
    • 2
  • Volker Blanz
    • 3
  • Katrin Dedden
    • 4
  • Ralph Miller
    • 5
  1. 1.University of MassachusettsAmherstUSA
  2. 2.Christie HospitalManchesterUK
  3. 3.Universität SiegenGermany
  4. 4.Max-Planck Institut für Informatik 
  5. 5.University of Kentucky Medical CenterLexingtonUSA

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