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Proceedings Paper

Improving specificity to image features for segmentation with deformable models
Author(s): Jens von Berg; Vladimir Pekar; Roel Truyen; Steven Lobregt; Michael R. Kaus
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Paper Abstract

The model of image features is critical to the robustness and accuracy of deformable models. Usually, an edge detector is used for this purpose, because the object boundary is expected to correspond with a strong directed gradient in the image. Two methods are presented to make a feature model more specific and suitable for a given object class for which this assumption is too weak. One aims at a better conformance of the model with the image features by a spatially varying parameterisation of clustered features that is learnt from a training set. The other discriminates the object surface from adjacent false attractors that have similar gradient properties by additional grey value properties. The clustered feature model was successfully applied in left ventricle segmentation to delineate the epicardium in cardiac MR images for which the image gradient reverses sign along the surface. The discriminating feature approach successfully prevented false attractions in CT bone segmentation to strong edges within other nearby bones (shown for femur head). In this case, the grey value beyond the attempted gradient position discriminated well the desired bone surface edges from these false edges.

Paper Details

Date Published: 15 May 2003
PDF: 10 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481346
Show Author Affiliations
Jens von Berg, Philips Research Labs. (Germany)
Vladimir Pekar, Philips Research Labs. (Germany)
Roel Truyen, Philips Medical Systems (Netherlands)
Steven Lobregt, Philips Medical Systems (Netherlands)
Michael R. Kaus, Philips Research Labs. (Germany)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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