Paper
14 March 2011 Direction-dependent level set segmentation of cerebrovascular structures
Author Affiliations +
Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79623S (2011) https://doi.org/10.1117/12.877942
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
Exact cerebrovascular segmentations based on high resolution 3D anatomical datasets are required for many clinical applications. A general problem of most vessel segmentation methods is the insufficient delineation of small vessels, which are often represented by rather low intensities and high surface curvatures. This paper describes an improved direction-dependent level set approach for the cerebrovascular segmentation. The proposed method utilizes the direction information of the eigenvectors computed by vesselness filters for adjusting the weights of the internal energy depending on the location. The basic idea of this is to weight the internal energy lower in case the gradient of the level set is comparable to the direction of the eigenvector extracted by the vesselness filter. A quantitative evaluation of the proposed method based on three clinical Time-of-Flight MRA datasets with available manual segmentations using the Tanimoto coefficient showed that a mean improvement compared to the initial segmentation of 0.081 is achieved, while the corresponding level set segmentation without integration of direction information does not lead to satisfying results. In summary, the proposed method enables an improved delineation of small vessels, especially of those represented by low intensities and high surface curvatures.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nils Daniel Forkert, Dennis Säring, Till Illies, Jens Fiehler, Jan Ehrhardt, Heinz Handels, and Alexander Schmidt-Richberg "Direction-dependent level set segmentation of cerebrovascular structures", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623S (14 March 2011); https://doi.org/10.1117/12.877942
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Cited by 8 scholarly publications.
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KEYWORDS
Image segmentation

Fuzzy logic

Image enhancement

Visualization

3D image processing

3D-TOF imaging

Brain

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