Percutaneous Treatment of Left Main Coronary Stenosis

Molina, J. Ernesto; Ellis, Stephen G.; Tamai, Hideo; Nobuyoshi, M.; Kosuga, Kunihiko; Colombo, Andrea; Holmes, D. R.; Macaya, Carlos; Grines, Cindy L.; Whitlow, Patrick L.; White, Harvey; Moses, Jeffrey J.; Teirstein, Paul S.; Serruys, Patrick W.; Bittl, John A.; Mooney, M.; Shimshak, Thomas M.; Block, P C; Erbel, Raimund

doi:10.1161/01.cir.98.15.1587

Cited by 7 publications

(1 citation statement)

References 8 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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