3-D reconstruction of coronary arteries

Sarwal, Alok; Dhawan, Atam P.

doi:10.1109/iembs.1994.411932

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…A wide variety of automatic blood vessel segmentation methods have been proposed in the last two decades. These methods used approaches that varied from Pattern Recognition techniques [3]- [11], Model-based Approaches [12]- [18], Tracking-Based Approaches [19]- [22], Artificial Intelligence Approaches [23]- [26] until Neural Networkbased approaches [27]- [29]. In [2], Kirbas and Queek presented a well referenced survey of some of those vessel segmentations methods.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography

Lara

Faria

Araújo

et al. 2009

2009 XXII Brazilian Symposium on Computer Graphics and Image Processing

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Nowadays, medical diagnostics using images has a considerable importance in many areas of medicine. It promotes and makes easier the acquisition, transmission and analysis of medical images. The use of digital images for diseases evaluations or diagnostics is still growing up and new application modalities are always appearing. This paper presents a methodology for a semi-automatic segmentation of the coronary artery tree in 2D X-Ray angiographies. It combines a region growing algorithm and a differential geometry approach. The proposed segmentation method identifies about 90% of the main coronary artery tree.

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Section: Introductionmentioning

confidence: 99%

A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography

Lara

Faria

Araújo

et al. 2009

2009 XXII Brazilian Symposium on Computer Graphics and Image Processing

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“…There are numerous publications in this area [Sarwal and Dhawan 1994;Zhang et al 2007;Yu and Bajaj ;Tozaki et al 1996;Sato et al 1998] and an extensive review on the subject is given by Kirbas, et al [2004]. These algorithms generally rely on filtering to remove highfrequency noise followed by isosurface extraction.…”

Section: Previous Workmentioning

confidence: 99%

Filament tracking and encoding for complex biological networks

Mayerich¹,

Keyser²

2008

Proceedings of the 2008 ACM Symposium on Solid and Physical Modeling

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Figure 1: Traced and segmented microvasculature of the mouse spinal cord. The entire network has been traced and displayed (orange) as well as several sub-networks (green and purple) represented as cliques within the network. AbstractWe present a framework for segmenting and storing filament networks from scalar volume data. Filament structures are commonly found in data generated using high-throughput microscopy. These data sets can be several gigabytes in size because they are either spatially large or have a high number of scalar channels. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet single filaments can span large data sets. 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 a GPU-based scheme to accelerate the tracing algorithm, making it more useful for large data sets. After the initial structure is traced, we can use this information to create a bounding volume around the network and encode the volumetric data associated with it. Taken together, this framework provides a convenient method for accessing network structure and connectivity while providing compressed access to the original volumetric data associated with the network.

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Coronary Artery Segmentation in Angiograms with Pattern Recognition Techniques -- A Survey

Tayebi

Sulaiman

Rahmat

et al. 2013

2013 International Conference on Advanced Computer Science Applications and Technologies

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Medical image processing is nowadays one of the best tools to make an informative model from a raw image of each part of the body, and segmentation is the most important step in which used to extract significant features. Coronary artery segmentation algorithm in angiograms is a fundamental component of each cardiac image processing system. There are lots of techniques and algorithms proposed for extracting coronary arteries in angiograms. But based on our knowledge, there is not any review paper to categorize and compare them together. In this paper, we have divided these algorithms into five major classes and propose a survey for the main class, pattern recognition, which is a famous technique in this manner. We studied all the papers in the pattern recognition class and defined six categories for them:(1) Multi scale approaches (2) Region growing approaches (3) Matching filters approaches (4) Mathematical morphology approaches (5) Skeleton based approaches and (6) Ridge based approaches. Finally, we made a table to compare all the algorithms in each category against criteria such as: user interaction, angiography types, dimensionality, enhancement method, full coronary artery output, whole tree output, and 3D reconstruction ability.

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3-D reconstruction of coronary arteries

Cited by 6 publications

References 3 publications

A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography

A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography

Filament tracking and encoding for complex biological networks

Coronary Artery Segmentation in Angiograms with Pattern Recognition Techniques -- A Survey

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