FAME - A Flexible Appearance Modelling Environment |
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| Abstract | Combined modelling of pixel intensities and shape has proven to be a very robust and widely applicable approach to interpret images. As such the Active Appearance Model (AAM) framework has been applied to a wide variety of problems within medical image analysis. This paper summarises AAM applications within medicine and describes a public domain implementation, namely the Flexible Appearance Modelling Environment (FAME). We give guidelines for the use of this research platform, and show that the optimisation techniques used renders it applicable to interactive medical applications. To increase performance and make models generalise better, we apply parallel analysis to obtain automatic and objective model truncation. Further, two different AAM training methods are compared along with a reference case study carried out on cross-sectional short-axis cardiac magnetic resonance images and face images. Source code and annotated data sets needed to reproduce the results are put in the public domain for further investigation. |
| Keywords | Active Appearance Models, public domain training data and software, left ventricular segmentation, face segmentation |
| Type | Journal paper [With referee] |
| Journal | IEEE Transactions on Medical Imaging |
| Editors | Max A. Viergever |
| Year | 2003 Vol. 22 No. 10 pp. 1319-1331 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Address | Piscataway, NJ, USA |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |