Biomedical subjects

H C van Assen

Publications and source records attributed to H C van Assen.

3 recordsLinked to original sources

PubMed2005

Optimizing the automatic segmentation of the left ventricle in magnetic resonance images.

Automatic segmentation of the left ventricular (LV) myocardial borders in cardiovascular MR (CMR) images allows a significant speed-up of the procedure of quantifying LV function, and improves its reproducibility. The automated boundary delineation is usually based on a set of parameters that define the algorithms. Since the automatic segmentation algorithms are usually sensitive to the image quality and frequently depend heavily on the acquisition protocol, optimizing the parameters of the algorithm for such different protocols may be necessary to obtain optimal results. In other words, using a default set of parameters may be far from optimal for different scanners or protocols. For the MASS-software, for example, this means that a total of 14 parameters need to be optimized. This optimization is a difficult and labor-intensive process. To be able to more consistently and rapidly tune the parameters, an automated optimization system would be extremely desirable. In this paper we propose such an approach, which is based on genetic algorithms (GAs). The GA is an unsupervised iterative tool that generates new sets of parameters and converges toward an optimal set. We implemented and compared two different types of the genetic algorithms: a simple GA (SGA) and a steady state GA (2SGA). The difference between these two algorithms lies in the characteristics of the generated populations: "nonoverlapping populations" and "overlapping populations," respectively "nonoverlapping" population means that the two populations are disjoint, and "overlapping" means that the best parameters found in the previous generation are included in the present population. The performance of both algorithms was evaluated on twenty routinely obtained short-axis examinations (eleven examinations acquired with a steady-state free precession pulse sequence, and nine examinations with a gradient echo pulse sequence). The optimal parameters obtained with the GAs were used for the LV myocardial border delineation. Finally, the automatically outlined contours were compared to the gold standard--manually drawn contours by experts. The result of the comparison was expressed as a degree of similarity after a processing time of less than 72 h to a 59.5% of degree of similarity for SGA and a 66.7% of degree of similarity for 2SGA. In conclusion, genetic algorithms are very suitable to automatically tune the parameters of a border detection algorithm. Based on our data, the 2SGA was more suitable than the SGA method. This approach can be generalized to other optimization problems in medical image processing.

Algorithms↗

PubMed2005

Parametric optimization of a model-based segmentation algorithm for cardiac MR image analysis: a grid-computing approach.

In this work we present a Grid-based optimization approach performed on a set of parameters that affects both the geometric and grey-level appearance properties of a three-dimensional model-based algorithm for cardiac MRI segmentation. The search for optimal values was assessed by a Monte Carlo procedure using computational Grid technology. A series of segmentation runs were conducted on an evaluation database comprising 30 studies at two phases of the cardiac cycle (60 datasets), using three shape models constructed by different methods. For each of these model-patient combinations, six parameters were optimized in two steps: those which affect the grey-level properties of the algorithm first and those relating to the geometrical properties, secondly. Two post-processing tasks (one for each stage) collected and processed (in total) more than 70000 retrieved result files. Qualitative and quantitative validation of the fitting results indicates that the segmentation performance was greatly improved with the tuning. Based on the experienced benefits with the use of our middleware, and foreseeing the advent of large-scale tests and applications in cardiovascular imaging, we strongly believe that the use of Grid computing technology in medical image analysis constitutes a real necessity.

Algorithms↗

PubMed2000

Vessel diameter measurements in gadolinium contrast-enhanced three-dimensional MRA of peripheral arteries.

In this study, the possibilities for quantification of vessel diameters of peripheral arteries in gadolinium contrast-enhanced magnetic resonance angiography (Gd CE MRA) were evaluated. Absolute vessel diameter measurements were assessed objectively and semi-automatically in maximum intensity projections (MIPs) of contrast-enhanced T1-weighted 3D spoiled gradient-echo datasets, studied with digital subtraction techniques. In vivo, the complete peripheral arterial bed of six patients was studied, from the aorto-iliac bifurcation down to the distal run-off. By measuring the signal intensity (SI) over the lumen of a vessel in the MIP, an SI-plot was obtained. Next, the vessel boundaries were determined using a threshold algorithm; from these boundary points individual diameter values could be obtained along the trajectory of the vessel. In an in vitro study, an optimal threshold value of 30% of the range of SI-values between the background and the maximal SI in the vessel was obtained for accurate diameter measurement in Gd CE MRA (i.e., full-width 30%-maximum). Furthermore, the relationship between the accuracy of these measurements and the scan resolution was investigated. Accuracy was found to be acceptable (i.e., less than 10% over/underestimation) for vessel sizes covering at least 3 pixels. In six patients, diameters were measured in MIPs of the total datasets (i.e., D(T)) as well as in selective MIPs of the clipped datasets (i.e., D(S)) (n = 209). D(T) and D(S) were statistically significantly correlated (p < 0.01) with a Pearson correlation coefficient rP = 0.98. Measurements in the total MIPs yielded statistically significant (p < 0.01) smaller diameter values compared with measurements in selective MIPs, with a mean difference of 0.15 mm. Diameter values from the selective MIPs of the aorto-iliac arteries were also compared with diameter values measured at corresponding anatomic positions in X-ray angiograms of these patients (i.e., D(x)) (n = 70). D(X) and D(S) were statistically significantly correlated (p < 0.01) with a Pearson correlation coefficient rP = 0.92. Diameters measured in the selective MIPs were smaller than those measured in the X-ray angiograms (mean difference 0.49 mm) and this difference was statistically significant (p < 0.01). In conclusion, diameter values can be evaluated accurately in MIPs of vessels with at least 3 pixels in diameter, using the full-width 30%-maximum criterion.

Adult↗