Marcel Breeuwer

System for subband coding of a digital audio signal

A system for subband coding of a digital audio signal x(k) includes in the coder (1) a filter bank (3) for split ting the audio signal band, with sampling rate reduction, into subbands (p = 1, . . . P) of approximately critical bandwidth and in the decoder (2) a filter bank (5) for merging these subbands, with sampling rate increase. For each subband (p) the coder (1) comprises a detector (7(p)) for determining a parameter G(p;m) representa tive of the signal level in a block (p;m) of Msamples of the subband signal x(k) as well as a quantizer (80p)) for adaptively block quantizing this subband signal in re sponse to parameter G(p;m), and the decoder (2) com prises a dequantizer (9(p)) for adaptively block dequan tizing the quantized subband signal s(k) in response to parameter G(p;m). The quantizing characteristics are related to the noise-masking curve of the human audi tory system, owing to which a high-quality of the rep lica 3(k) of audio signal x(k) is attained with an average number of approximately 2.5 bits per sample for repre senting the oitput signals of the coder (1). The occa sional audibility of quantizing noise in this replica 3(k) is reduced effectively in that the coder (1) and decoder (2) contain identical bit allocation means (23, 24) respon sive to a set of parameters G(p;m) for the higher group of subbands (pin Sps P) within an allocation window (FIG. 5) for allocating a number of B(p;m) bits per sample from a fixed predetermined number of B bits for this allocation window to the quantizer (8(p)) and the dequantizer (9(p)) for the block (p;m) of subband signal x(k) and sp(k), respectively.

The mechanical role of thrombus on the growth rate of an abdominal aortic aneurysm

OBJECTIVES In the decision for surgical repair of abdominal aortic aneurysms (AAAs), the maximum diameter is the main factor. Several studies have concluded that the diameter may not be reliable as rupture risk criterion for the individual patient and wall stress was found to have a higher sensitivity and specificity. The AAA wall stress may also be an influential factor in growth of the AAA. This study investigates the effect of intraluminal thrombus on the wall stress and growth rate of aneurysms, using both idealized and patient-specific AAA models in wall stress computations. METHODS Idealized AAA models were created for wall stress analysis. Thrombus was modeled as an incompressible linear elastic material and was fixed to the wall. The reduction in wall stress for a range of thrombus volumes and shear moduli was computed. For 30 patient-specific AAA models with varying thrombus volumes, the wall stress was computed with and without thrombus. The diameter growth rate was compared for AAAs with a small and large thrombus volume. The results were compared between the idealized and patient-specific models. RESULTS The thrombus caused a reduction in wall stress, which was stronger for larger thrombi and higher elastic moduli. Any AAAs with a large thrombus were found to have significant stronger growth in diameter than aneurysms with a small thrombus (P < .01). The stress reduction due to the thrombus showed the same trend for the idealized and patient-specific models, although the effect was overestimated by the idealized models and a considerable variation between patients was observed. CONCLUSION A larger thrombus in AAA was associated with a higher AAA growth rate, but also with a lower wall stress. Therefore, weakening of the AAA wall, under the influence of thrombus, may play a more imminent role in the process of AAA growth than the stress acting on the wall.

Automatic Contour Propagation in Cine Cardiac Magnetic Resonance Images

We have developed a method for automatic contour propagation in cine cardiac magnetic resonance images. The method consists of a new active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours should maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, we describe how to use full factorial experiments to optimize the parameter setting. We have applied our method to cine cardiac magnetic resonance image sequences from the long axis two-chamber view, the long axis four-chamber view, and the short axis view. We performed our optimization procedure for each contour in each view. Next, we performed an extensive clinical validation of our method on 69 short axis data sets and 38 long axis data sets. In the optimal parameter setting, our propagation method proved to be fast, robust, and accurate. The resulting cardiac contours are positioned within the interobserver ranges of manual segmentation. Consequently, the resulting contours can be used to accurately determine physiological parameters such as stroke volume and ejection fraction

MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling

This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (two thresholds). For the more complex problem of thrombus segmentation, a grey level modeling approach with a nonparametric pattern classification technique is used, namely k-nearest neighbors. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding to the intensity profile at each vertex on the surface. A parameter optimization study is conducted, followed by experiments to assess the overall segmentation quality and the robustness of results against variation in user input. Results obtained in a study of 17 patients show that the agreement with respect to manual segmentations is comparable to previous values reported in the literature, with considerable less user interaction.

System for subband coding of a digital audio signal and coder and decoder constituting the same

A system for subband coding of a digital audio signal x(k) includes in the coder (1) a filter bank (3) for splitting the audio signal band, with sampling rate reduction, into subbands (p=1, . . . P) of approximately critical bandwidth and in the decoder (2) a filter bank (5) for merging these subbands, with sampling rate increase. For each subband (p) the coder (1) comprises a detector (7(p)) for determining a parameter G(p;m) representative of the signal level in a block (p;m) of M samples of the subband signal xp (k) as well as a quantizer (8(p)) for adaptively block quantizing this subband signal in response to parameter G(p;m), and the decoder (2) comprises a dequantizer (9(p)) for adaptively block dequantizing the quantized subband signal sp (k) in response to parameter G(p;m). The quantizing characteristics are related to the noise-masking curve of the human auditory system, owing to which a high-quality of the replica x(k) of audio signal x(k) is attained with an average number of approximately 2.5 bits per sample for representing the output signals of the coder (1). The occasional audibility of quantizing noise in this replica x(k) is reduced effectively in that the coder (1) and decoder (2) contain identical bit allocation means (23, 24) responsive to a set of parameters G(p;m) for the higher group of subbands (pim ≦p≦P) within an allocation window (FIG. 5) for allocating a number of B(p;m) bits per sample from a fixed predetermined number of B bits for this allocation window to the quantizer (8(p)) and the dequantizer (9(p)) for the block (p;m) of subband signal xp (k) and sp (k), respectively.

Initial stress and nonlinear material behavior in patient-specific AAA wall stress analysis.

Rupture risk estimation of abdominal aortic aneurysms (AAA) is currently based on the maximum diameter of the AAA. A more critical approach is based on AAA wall stress analysis. For that, in most cases, the AAA geometry is obtained from CT-data and treated as a stress free geometry. However, during CT imaging, the AAA is subjected to a time-averaged blood pressure and is therefore not stress free. The aim of this study is to evaluate the effect of neglecting these initial stresses (IS) on the patient-specific AAA wall stress as computed by finite element analysis. Additionally, the contribution of the nonlinear material behavior of the AAA wall is evaluated. Thirty patients with maximum AAA diameters below the current surgery criterion were scanned with contrast-enhanced CT and the AAAs were segmented from the image data. The mean arterial blood pressure (MAP) was measured immediately after the CT-scan and used to compute the IS corresponding with the CT geometry and MAP. Comparisons were made between wall stress obtained with and without IS and with linear and nonlinear material properties. On average, AAA wall stresses as computed with IS were higher than without IS. This was also the case for the stresses computed with the nonlinear material model compared to the linear material model. However, omitting initial stress and material nonlinearity in AAA wall stress computations leads to different effects in the resulting wall stress for each AAA. Therefore, provided that other assumptions made are not predominant, IS cannot be discarded and a nonlinear material model should be used in future patient-specific AAA wall stress analyses.

CoViCAD: Comprehensive Visualization of Coronary Artery Disease

We present novel, comprehensive visualization techniques for the diagnosis of patients with coronary artery disease using segmented cardiac MRI data. We extent an accepted medical visualization technique called the bulls eye plot by removing discontinuities, preserving the volumetric nature of the left ventricular wall and adding anatomical context. The resulting volumetric bulls eye plot can be used for the assessment of transmurality. We link these visualizations to a 3D view that presents viability information in a detailed anatomical context. We combine multiple MRI scans (whole heart anatomical data, late enhancement data) and multiple segmentations (polygonal heart model, late enhancement contours, coronary artery tree). By selectively combining different rendering techniques we obtain comprehensive yet intuitive visualizations of the various data sources.

Exploration of 4D MRI Blood Flow using Stylistic Visualization

Insight into the dynamics of blood-flow considerably improves the understanding of the complex cardiovascular system and its pathologies. Advances in MRI technology enable acquisition of 4D blood-flow data, providing quantitative blood-flow velocities over time. The currently typical slice-by-slice analysis requires a full mental reconstruction of the unsteady blood-flow field, which is a tedious and highly challenging task, even for skilled physicians. We endeavor to alleviate this task by means of comprehensive visualization and interaction techniques. In this paper we present a framework for pre-clinical cardiovascular research, providing tools to both interactively explore the 4D blood-flow data and depict the essential blood-flow characteristics. The framework encompasses a variety of visualization styles, comprising illustrative techniques as well as improved methods from the established field of flow visualization. Each of the incorporated styles, including exploded planar reformats, flow-direction highlights, and arrow-trails, locally captures the blood-flow dynamics and may be initiated by an interactively probed vessel cross-section. Additionally, we present the results of an evaluation with domain experts, measuring the value of each of the visualization styles and related rendering parameters.

Patient-Specific AAA Wall Stress Analysis: 99-Percentile Versus Peak Stress

OBJECTIVE Biomechanically, rupture of an Abdominal Aortic Aneurysm (AAA) occurs when the stress acting on the wall due to the blood pressure, exceeds the strength of the wall. Peak wall stress estimations, based on CT reconstruction, may be prone to observer variation. This study focuses on the robustness and reproducibility of AAA wall stress assessment and the relation with geometrical features of the AAA. METHODS The AAAs of twenty patients were reconstructed by three operators. Both the peak and 99-percentile stress were used for intra- and inter-operator variability using the intraclass correlation coefficient (ICC). A regression analysis was performed to relate the stress parameters with the maximum diameter. Outliers were analyzed by their geometrical characteristics. RESULTS The intra-operator ICC was 0.73-0.79 for the peak stress and 0.94 for the 99-percentile stress. The inter-operator ICC was 0.71 for the peak stress and 0.95 for the 99-percentile stress. A significant linear relation with the diameter was found only for the 99-percentile stress. CONCLUSIONS The 99-percentile stress is more reproducible than peak wall stress. A significant relation between wall stress and diameter was found. Other geometrical features had no statistical relation with high stress.