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

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

Biocybernetics and Biomedical Engineering

2017

tom 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.

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

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

tom 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.