Deformable models in medical image analysis: A survey

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Deformable models in medical image analysis: A survey (1996)

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by Tim Mcinerney , Demetri Terzopoulos

Venue:	Medical Image Analysis
Citations:	591 - 7 self

BibTeX

@ARTICLE{Mcinerney96deformablemodels,
    author = {Tim Mcinerney and Demetri Terzopoulos},
    title = {Deformable models in medical image analysis: A survey},
    journal = {Medical Image Analysis},
    year = {1996},
    volume = {1},
    pages = {91--108}
}

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Abstract

This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, includingsegmentation, shape representation, matching, and motion tracking.

Keyphrases

deformable model medical image analysis different individual priori knowledge fundamental importance computer-assisted medical image analysis technique motion tracking anatomic structure powerful approach model-based technique intuitive interaction mechanism significant variability shape representation image data approximation theory biological structure medical scientist image analysis model-based image interpretation task

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