Hands-on experience with active appearance models

Hans Henrik Thodberg

AbstractThe aim of this work is to explore the performance of Active Appearance Models (AAMs) in reconstruction and
interpretation of bones in hand radiographs. AAM is a generative approach that unifies image segmentation and image
understanding. Initial locations for the AAM search are generated by an exhaustive filtering method. A series of AAMs
for smaller groups of bones are used. It is found that AAM successful reconstructs 99% of metacarpals, proximal and
medial phalanges and the distal 3 cm of radius and ulna. The rms accuracy is better than 240 microns (point-to-curve).
The generative property is used (1) to define a measure of fit that allows the models to self-evaluate and chose between
the multiple found solutions, (2) to overcome obstacles in the image in the form of rings by predicting the missing part,
and (3) to detect anomalies, e.g. rheumatoid arthritis. The shape scores are used as a biometrics to check the identity of
patients in a longitudinal study. The conclusion is that AAM provides a highly efficient and unified framework for
various tasks in diagnosis and assessment of bone related disorders.
KeywordsActive appearance model, shape models, segmentation, image understanding, arthritis, osteoporosis
TypeConference paper [With referee]
ConferenceMedical Imaging 2002: Image Processing
EditorsMilan Sonka and Michael J. Fitzpatrick
Year2002    Vol. 4684    pp. 495-506
SeriesProceedings of SPIE
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics