Statistical models of appearance for medical image analysis and computer vision

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding 'landmark' points have been marked on every image. From this data we can compute a a statistical model of the shape variation, a model of the texture variation and a model of the correlations between shape and texture. With enough training examples such models should be able to synthesize any image of normal anatomy. By finding the parameters which optimize the match between a synthesized model image and a target image we can locate all the structures represented by the model. Two approaches to the matching will be described. The Active Shape Model essentially matches a model to boundaries in an image. The Active Appearance Model finds model parameters which synthesize a complete image which is as similar as possible to the target image. By using a 'difference decomposition' approach the current difference between target image and synthesized model image can be used to update the model parameters, leading to rapid matching of complex models. We will demonstrate the application of such models to a variety of different problems.
    Original languageEnglish
    Title of host publicationProceedings of SPIE - The International Society for Optical Engineering|Proc SPIE Int Soc Opt Eng
    EditorsM. Sonka, K.M. Hanson
    PublisherSPIE
    Pages236-248
    Number of pages12
    Volume4322
    DOIs
    Publication statusPublished - 2001
    EventMedical Imaging 2001 Image Processing - San Diego, CA
    Duration: 1 Jul 2001 → …

    Publication series

    NameProceedings of SPIE
    PublisherSPIE

    Conference

    ConferenceMedical Imaging 2001 Image Processing
    CitySan Diego, CA
    Period1/07/01 → …

    Keywords

    • Appearance models
    • Model matching
    • Shape models

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