Three dimensional reconstruction of coronary arteries from two views

Sarwal, Alok; Dhawan, Atam P.

doi:10.1109/iembs.1997.757672

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…These focus on the segmentating large blood vessels, often using extensive image processing as an initial step [25,26]. Multi-scale techniques [19,3] can be used for feature detection, however most of the filaments in high-throughput microscopy data are thin, requiring the highest level of detail to resolve. This makes multi-scale segmentation impractical since the resulting down-sampling destroys filament information.…”

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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“…It is difficult, however, to digitize the human body because of its intricacy. The research on digital visible human rendering, therefore, is of great significance and has many valuable applications (Sarwal and Dhawan, 2001;Temkin, 2002). In medicine, for example, a visible human can be accessed regardless of time and place, only requiring a computer and the appropriate digital medium (recorded or streaming).…”

Section: Introductionmentioning

confidence: 99%

Creation of the Chinese visible human data set

Zhang

Heng²,

Liu³

et al. 2003

The Anatomical Record Part B

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“…In case of SIRT, the ability to display the blood vessels along with the liver and the tumor/tumors in a 3-D space offers an idea of the extent of the vasculature feeding the tumor as well as provides a map for the insertion point of the radio-isotope (Gulec and Fong, 2007 (Sarwal and Dhawan, 1997). Skeleton based approaches to extract and locate the centerline of the vessel tree have been used to extract blood vessel in CT.…”

Section: Review Of Hepatic Automatic/semi-automatic Blood Vessel Segmmentioning

confidence: 99%

A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning

Goryawala¹

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Three dimensional reconstruction of coronary arteries from two views

Cited by 3 publications

References 15 publications

Hardware Accelerated Segmentation of Complex Volumetric Filament Networks

Hardware Accelerated Segmentation of Complex Volumetric Filament Networks

Creation of the Chinese visible human data set

A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning

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