Improving specificity to image features for segmentation with deformable models

Jens von Berg; Vladimir Pekar; Roel Truyen; Steven Lobregt; Michael R. Kaus

doi:10.1117/12.481346

15 May 2003 Improving specificity of image features for segmentation with deformable models

Jens von Berg, Vladimir Pekar, Roel Truyen, Steven Lobregt, Michael R. Kaus

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.481346
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Jens von Berg, Vladimir Pekar, Roel Truyen, Steven Lobregt, and Michael R. Kaus "Improving specificity of image features for segmentation with deformable models", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481346

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