FAME-a flexible appearance modeling environment. Combined modeling 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 summarizes AAM applications within medicine and describes a public domain implementation, namely the flexible appearance modeling environment (FAME). We give guidelines for the use of this research platform, and show that the optimization techniques used renders it applicable to interactive medical applications. To increase performance and make models generalize 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.

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  2. Selvan, S. Easter; George, S. Thomas; Balakrishnan, R.: Range-based ICA using a nonsmooth quasi-Newton optimizer for electroencephalographic source localization in focal epilepsy (2015)
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  4. Del Bue, Alessio: Adaptive non-rigid registration and structure from motion from image trajectories (2013)
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  7. Nguyen, Tan Dat; Ranganath, Surendra: Facial expressions in American sign language: tracking and recognition (2012)
  8. Erbou, Søren G. H.; Vester-Christensen, Martin; Larsen, Rasmus; Christensen, Lars B.; Ersbøll, Bjarne K.: Comparison of sparse point distribution models (2010) ioport
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  10. Zhuang, Yue-Ting; Wang, Yu-Shun; Shih, Timothy K.; Tang, Nick C.: Patch-guided facial image inpainting by shape propagation (2009)
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