FAME - A Flexible Appearance Modelling Environment

Mikkel Bille Stegmann, Bjarne Kjær Ersbøll, Rasmus Larsen

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    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.
    Original languageEnglish
    JournalI E E E Transactions on Medical Imaging
    Issue number10
    Pages (from-to)1319 - 1331
    Publication statusPublished - 2003

    Bibliographical note

    Copyright: 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE


    • public domain training data and software
    • face segmentation
    • Active Appearance Models
    • left ventricular segmentation


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