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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  1. Lyu, Xing-long; Li, Tiexiang; Huang, Tsung-ming; Lin, Jia-wei; Lin, Wen-wei; Wang, Sheng: FAME. Fast algorithms for Maxwell’s equations for three-dimensional photonic crystals (2021)
  2. Kolouri, Soheil; Tosun, Akif B.; Ozolek, John A.; Rohde, Gustavo K.: A continuous linear optimal transport approach for pattern analysis in image datasets (2016)
  3. 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)
  4. Ban, Yuseok; Kim, Sang-Ki; Kim, Sooyeon; Toh, Kar-Ann; Lee, Sangyoun: Face detection based on skin color likelihood (2014) ioport
  5. Del Bue, Alessio: Adaptive non-rigid registration and structure from motion from image trajectories (2013)
  6. Selvan, S. Easter; Borckmans, Pierre B.; Chattopadhyay, A.; Absil, P.-A.: Spherical mesh adaptive direct search for separating quasi-uncorrelated sources by range-based independent component analysis (2013)
  7. Wang, Wei; Slepčev, Dejan; Basu, Saurav; Ozolek, John A.; Rohde, Gustavo K.: A linear optimal transportation framework for quantifying and visualizing variations in sets of images (2013)
  8. Nguyen, Tan Dat; Ranganath, Surendra: Facial expressions in American sign language: tracking and recognition (2012)
  9. Erbou, Søren G. H.; Vester-Christensen, Martin; Larsen, Rasmus; Christensen, Lars B.; Ersbøll, Bjarne K.: Comparison of sparse point distribution models (2010) ioport
  10. Lozano, Oscar Mateo; Otsuka, Kazuhiro: Real-time visual tracker by stream processing simultaneous and fast 3D tracking of multiple faces in video sequences by using a particle filter (2009) ioport
  11. Zhuang, Yue-Ting; Wang, Yu-Shun; Shih, Timothy K.; Tang, Nick C.: Patch-guided facial image inpainting by shape propagation (2009)
  12. Zheng, Zhonglong; Jiong, Jia; Chunjiang, Duanmu; Liu, Xinhong; Yang, Jie: Facial feature localization based on an improved active shape model (2008) ioport
  13. Langs, Georg; Peloschek, Philipp; Donner, René; Bischof, Horst: Multiple appearance models (2007)