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Biomedical subjects

Henk A Marquering

Publications and source records attributed to Henk A Marquering.

2 recordsLinked to original sources

Automatic initialization algorithm for carotid artery segmentation in CTA images.

Analysis of CT datasets is commonly time consuming because of the required manual interaction. We present a novel and fast automatic initialization algorithm to detect the carotid arteries providing a fully automated approach of the segmentation and centerline detection. First, the volume of interest (VOI) is estimated using a shoulder landmark. The carotid arteries are subsequently detected in axial slices of the VOI by applying a circular Hough transform. To select carotid arteries related signals in the Hough space, a 3-D, direction dependent hierarchical clustering is used. To allow a successful detection for a wide range of vessel diameters, a feedback architecture was introduced. The algorithm was designed and optimized using a training set of 20 patients and subsequently evaluated using 31 test datasets. The detection algorithm, including VOI estimation, correctly detects 88% of the carotid arteries. Even though not all carotid arteries have been correctly detected, the results are very promising.

Algorithms↗

Towards quantitative analysis of coronary CTA.

The current high spatial and temporal resolution, multi-slice imaging capability, and ECG-gated reconstruction of multi-slice computed tomography (MSCT) allows the non-invasive 3D imaging of opacified coronary arteries. MSCT coronary angiography studies are currently carried out by the visual inspection of the degree of stenosis and it has been shown that the assessment with sensitivities and specificities of 90% and higher can be achieved. To increase the reproducibility of the analysis, we present a method that performs the quantitative analysis of coronary artery diseases with limited user interaction: only the positioning of one or two seed points is required. The method allows the segmentation of the entire left or right coronary tree by the positioning of a single seed point, and an extensive evaluation of a particular vessel segment by placing a proximal and distal seed point. The presented method consists of: (1) the segmentation of the coronary vessels, (2) the extraction of the vessel centerline, (3) the reformatting of the image volume, (4) a combination of longitudinal and transversal contour detection, and (5) the quantification of vessel morphological parameters. The method is illustrated in this paper by the segmentation of the left and right coronary trees and by the analysis of a coronary artery segment. The sensitivity of the positioning of the seed points is studied by varying the position of the proximal and distal seed points with a standard deviation of 6 and 8 mm (along the vessel's course) respectively. It is shown that only close to the individual seed points the vessel centerlines deviate and that for more than 80% of the centerlines the paths coincide. Since the quantification depends on the determination of the centerline, no user variability is expected as long as the seed points are positioned reasonably far away from the vessel lesion. The major bottleneck of MSCT imaging of the coronary arteries is the potential lack of image quality due to limitations in the spatial and temporal resolution, irregular or high heart beat, respiratory effects, and variations of the distribution of the contrast agent: the number of rejected vessel segments in diagnostic studies is currently still too high for implementation in routine clinical practice. Also for the automated quantitative analysis of the coronary arteries high image quality is required. However, based upon the trend in technological development of MSCT scanners, there is no doubt that the quantitative analysis of MSCT coronary angiography will benefit from these technological advances in the near future.

Coronary Angiography↗