3D Active Appearance Models for Segmentation of Cardiac MRI Data

Sebastian Zambal
3D Active Appearance Models for Segmentation of Cardiac MRI Data
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Abstract

Segmentation of volumetric medical data is extremely time-consuming if done manually. This is the reason why currently great efforts are being made to develop algorithms for automatic segmentation. Model based techniques represent one very promising approach. A model representing the object of interest is matched with unknown data. During the matching process the model’s shape and additional properties are varied in order to iteratively improve the match. As soon as the model fits sufficiently well to the data, the properties of the model can be mapped to the data and so a segmentation is derived. Recently the segmentation of cardiac magnetic resonance images (MRI) has been of great interest. In this work we outline some of the methods proposed to solve the problem of cardiac segmentation. We review Active Appearance Models (AAMs) which are a special type of deformable models. AAMs rule changes in shape and texture using statistical information obtained from a data base of representative examples. We describe the theory behind AAMs with special focus on 3D AAMs. These are applicable to volumetric medical image data. Our implementation of 3D AAMs is outlined and the results obtained for 3D segmentation of the left cardiac ventricle are presented.

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BibTeX

@mastersthesis{zambal-2005-3dact,
  title =      "3D Active Appearance Models for Segmentation of Cardiac MRI
               Data",
  author =     "Sebastian Zambal",
  year =       "2005",
  abstract =   "Segmentation of volumetric medical data is extremely
               time-consuming if done manually. This is the reason why
               currently great efforts are being made to develop algorithms
               for automatic segmentation. Model based techniques represent
               one very promising approach. A model representing the object
               of interest is matched with unknown data. During the
               matching process the model’s shape and additional
               properties are varied in order to iteratively improve the
               match. As soon as the model fits sufficiently well to the
               data, the properties of the model can be mapped to the data
               and so a segmentation is derived. Recently the segmentation
               of cardiac magnetic resonance images (MRI) has been of great
               interest. In this work we outline some of the methods
               proposed to solve the problem of cardiac segmentation. We
               review Active Appearance Models (AAMs) which are a special
               type of deformable models. AAMs rule changes in shape and
               texture using statistical information obtained from a data
               base of representative examples. We describe the theory
               behind AAMs with special focus on 3D AAMs. These are
               applicable to volumetric medical image data. Our
               implementation of 3D AAMs is outlined and the results
               obtained for 3D segmentation of the left cardiac ventricle
               are presented.",
  month =      aug,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2005/zambal-2005-3dact/",
}