Abstract
The automatic segmentation and labelling of anatomical structures in 3D medical images is a challenging task of practical importance. We describe a model-based approach which allows robust and accurate interpretation using explicit anatomical knowledge. Our method is based on the extension to 3D of Point Distribution Models (PDMs) and associated image search algorithms. A combination of global, Genetic Algorithm (GA), and local, Active Shape Model (ASM), search is used. We have built a 3D PDM of the human brain describing a number of major structures. Using this model we have obtained automatic interpretations for 30 3D Magnetic Resonance head images from different individuals. The results have been evaluated quantitatively and support our claim of robust and accurate interpretation.
Keywords
Computer scienceInterpretation (philosophy)SegmentationArtificial intelligencePoint distribution modelImage segmentationPoint (geometry)Extension (predicate logic)Task (project management)Computer visionPattern recognition (psychology)3d modelMathematics
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Publication Info
- Year
- 1993
- Type
- article
- Pages
- 34.1-34.10
- Citations
- 107
- Access
- Closed
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Cite This
Andrew Hill,
A. Thornham,
Chris Taylor
(1993).
Model-Based Interpretation of 3D Medical Images.
, 34.1-34.10.
https://doi.org/10.5244/c.7.34
Identifiers
- DOI
- 10.5244/c.7.34