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3D vascular tree segmentation using a multiscale vesselness function and a level set approach

Woźniak T., Strzelecki M., Majos A., Stefańczyk L.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 1

66--77

The paper presents a method aimed at segmentation of a vascular network in 3D medical data. The method implements an extended version of a vesselness function that considers multiscale image filtering to emphasize vessels of different diameters. This function is combined with a level set approach based on a Chan–Vese model. The proposed method was evaluated on medical images of the brain and hand vasculature. These images were obtained by different modalities, including angio-CT and two MR acquisition protocols. The proposed technique was quantitatively validated for the tree phantom image by assessing segmentation accuracy and for the angio-CT images by estimating diameters of vessel fragments. Two radiologists provided also qualitative evaluation of this approach. It was demonstrated that this method ensures correct segmentation of a vessel tree in the analyzed images. Moreover, it enables detection of thinner vessel branches when compared to single scale vesselness function approaches.

Level-set based segmentation of carotid arteries in computed tomography angiography images

Pięciak T., Baran M., Urbańczyk M.

Journal of Medical Informatics & Technologies

2011

Vol. 17

281--286

In this paper a segmentation algorithm of carotid arteries on computed tomography angiography (CTA) images is proposed. The algorithm is based on the threshold level set approach. In the basic version, the algorithm analyzes CTA slices beginning at the brachiocephalic trunk and going towards carotid arteries. Second variant of the algorithm performs segmentation in the opposite direction, which implies that the algorithm can follow branches e.g. subclavian arteries. The localization process of the initial contour, for threshold level set method, on the first slice is based on curvature anisotropic diffusion filter, the Gaussian filter and fast marching method. The article contains segmentation results for tested sets of method parameters. Experimental results show that optimal set of parameters ensuring that the threshold level set method performs segmentation of the entire subclavian arteries, does not exist.

3D watershed transform combined with a probabilistic atlas for medical image segmentation

Straka M., La Cruz A., Köchl A., Šrámek M., Gröller E., Fleischmann D.

Journal of Medical Informatics & Technologies

2003

Vol. 6

IT69--78

Recent advances in medical imaging technology using multiple detector-row computed tomography (CT) provide volumetric datasets with unprecedented spatial resolution. This has allowed for CT to evolve into an excellent non-invasive vascular imaging technology, commonly referred to as CT-angiography. Visualisation of vascular structures from CT datasets is demanding, however, and identification of anatomic objects in CT-datasets is highly desirable. Density and/or gradient operators have been used most commonly to classify CT data. In CT angiography, simple density/gradient operators do not allow precise and reliable classification of tissues due to the fact that different tissues (e.g. bones and vessels) possess the same density range and may lie in close spatial vicinity. We think, that anatomic classification can be achieved more accurately, if both spatial location and density properties of volume data are taken into account. We present a combination of two well-known methods for volume data processing to obtain accurate tissue classification. 3D watershed transform is used to partition the volume data in morphologically consistent blocks and a probabilistic anatomic atlas is used to distinguish between different kinds of tissues based on their density.