An X-ray-based method for the determination of the contrast agent propagation in 3-D vessel structures

Schmitt, Holger; Graß, Michael; Rasche, Volker; Schramm, Oliver; Haehnel, S.; Sartor, Klaus

doi:10.1109/42.996343

Cited by 43 publications

(24 citation statements)

References 7 publications

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“…In addition, thin filaments often drop below the sampling resolution, creating gaps in threshold-based segmentations (Figure 1c). Region growing approaches [21,16] require some initial surface, which is difficult to find given the complex topology of the embedded network. The methods that we describe in this paper could potentially be used as an initial condition for region growing approaches.…”

Section: Previous Workmentioning

confidence: 99%

Hardware Accelerated Segmentation of Complex Volumetric Filament Networks

Mayerich

Keyser

2009

IEEE Trans. Visual. Comput. Graphics

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Abstract-We present a framework for segmenting and storing filament networks from scalar volume data. Filament networks are encountered more and more commonly in biomedical imaging due to advances in high-throughput microscopy. These data sets are characterized by a complex volumetric network of thin filaments embedded in a scalar volume field. High-throughput microscopy volumes are also difficult to manage since they can require several terabytes of storage, even though the total volume of the embedded structure is much smaller. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet these networks can span large regions of the data set. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and under-sampled data. We use graphics hardware to accelerate the tracing algorithm, making it more useful for large data sets. After the initial network is traced, we use an efficient encoding scheme to store volumetric data pertaining to the network.

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Section: Previous Workmentioning

confidence: 99%

Hardware Accelerated Segmentation of Complex Volumetric Filament Networks

Mayerich

Keyser

2009

IEEE Trans. Visual. Comput. Graphics

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“…Two major conditions looked for in region growing methods are intensity value similarity and spatial proximity. 22,23 Region growing methods also include an initial thresholding. Deformable model methods include active contours or snakes, level sets and wave-front propagation.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis

et al. 2014

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The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80% ± 2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94% ± 2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

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“…Besides classical fusion processes [3,25] and region growing algorithm [7,21], tracking-based approaches such as [18,13,20] are an important attempt to to address this issue. They are based on a propagation inside the tubular structure from a known starting point by using a local search.…”

Section: Introductionmentioning

confidence: 99%