A reconstruction platform for coronary arteries, finite element mesh generation and patient specific simulations

Salles, Sabina; Salvucci, Fernando; Craiem, Damián

doi:10.1088/1742-6596/332/1/012048

Cited by 2 publications

(3 citation statements)

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“…Tracking method is proposed in [9] for tracking coronary arteries in CTA volume through 2D slices from ostium to the end, this method works based on the fact that each coronary artery has continuous pixels through slices from the start to the end point. In [10], volumetric region-growing algorithm used to segment the left coronary arteries tree, a sequential 3D thinning algorithm used as skeletonization method to extract arteries centerlines. Fig.…”

Section: D-semi-automatic Methodsmentioning

confidence: 99%

“…In [5]- [10] most recent 2D automatic segmentation methods are presented. The main advantage of these methods is minimal user interaction so less time and less human effort, however, the accuracy needs to be better.…”

Section: D-semi-automatic Methodsmentioning

confidence: 99%

“…Some of these methods are useful in coronary arteries segmentation. It is seen from the literature review that many techniques have been proposed using the 3D cardiac CTA volume dataset [5]- [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Fuzzy-based Hybrid Approach for Segmentation and Centerline Extraction of Main Coronary Arteries

Khaled¹,

Shalaby²,

Sayed³

2017

ijacsa

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Coronary arteries segmentation and centerlines extraction is an important step in Coronary Artery Disease diagnosis. The main purpose of the fully automated presented approaches is helping the clinical non-invasive diagnosis process to be done in fast way with accurate result. In this paper, a hybrid scheme is proposed to segment the coronary arteries and to extract the centerlines from Computed Tomography Angiography volumes. The proposed automatic hybrid segmentation approach combines the Hough transform with a fuzzy-based region growing algorithm. First, a circular Hough transform is used to detect initially the aorta circle. Then, the well-known Fuzzy c-mean algorithm is employed to detect the seed points for the region growing algorithm resulting in 3D binary volume. Finally, the centerlines of the segmented arteries are extracted based on the segmented 3D binary volume using a skeletonization based method. Using a benchmark database provided by the Rotterdam Coronary Artery Algorithm Evaluation Framework, the proposed algorithm is tested and evaluated. A comparative study shows that the proposed hybrid scheme is able to achieve a higher accuracy, in comparison to the most related and recent published work, at reasonable computational cost.

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Section: D-semi-automatic Methodsmentioning

confidence: 99%

Section: D-semi-automatic Methodsmentioning

confidence: 99%