We present a method for visualizing anatomic tree structures, such as vasculature and bronchial trees based on clinical CT- or MR data. The vessel skeleton as well as the diameter information per voxel serve as input. Our method adheres to these data, while producing smooth transitions at branchings and closed, rounded ends by means of convolution surfaces. We discuss the filter design with respect to irritating bulges, unwanted blending and the correct visualization of the vessel diameter. Similar to related work our method is based on the assumption of a circular cross-section of vasculature. In contrast to other authors we employ implicit surfaces to achieve high quality visualization. The method has been applied to a large variety of anatomic trees and produces good results.

Abstract—An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%–21%. Paired-tests showed that improvements are statistically significant @ H HHHSA. Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated @ aHUVA. Index Terms—Image segmentation, minimum description length, optimization methods, segmentation evaluation. I.

Accurate estimation of vessel parameters is a prerequisite for automated visualization and analysis of normal and diseased blood vessels. The objective of this research is to estimate the dimensions of lower extremity arteries, imaged by computed tomography (CT). The vessel is modeled using an elliptical or cylindrical structure with specific dimensions, orientation and blood vessel mean density. The model separates two homogeneous regions: Its inner side represents a region of density for vessels, and its outer side a region for background. Taking into account the point spread function (PSF) of a CT scanner, a function is modeled with a Gaussian kernel, in order to smooth the vessel boundary in the model. A new strategy for vessel parameter estimation is presented. It stems from vessel model and model parameter optimization by a nonlinear optimization procedure (the Levenberg-Marquardt technique). The method provides center location, diameter and orientation of the vessel as well as blood and background mean density values. The method is tested on synthetic data and real patient data with encouraging results.

Abstract—In this paper, we present a visualization system for the visual analysis of PET/CT scans of aortic arches of mice. The system has been designed in close collaboration between researchers from the areas of visualization and molecular imaging with the objective to get deeper insights into the structural and molecular processes which take place during plaque development. Understanding the development of plaques might lead to a better and earlier diagnosis of cardiovascular diseases, which are still the main cause of death in the western world. After motivating our approach, we will briefly describe the multimodal data acquisition process before explaining the visualization techniques used. The main goal is to develop a system which supports visual comparison of the data of different species. Therefore, we have chosen a linked multi-view approach, which amongst others integrates a specialized straightened multipath curved planar reformation and a multimodal vessel flattening technique. We have applied the visualization concepts to multiple data sets, and we will present the results of this investigation. Index Terms—Vessel visualization, plaque growth, multipath CPR, vessel flattening.

The coronary arteries are essential for the proper function of the human heart. However, they are generally difficult to segment in volume datasets separately from the other blood-filled cavities of the heart. The major reason for these difficulties is the lack of sufficient spatial resolution and partial volume effects. In this paper, we present a method to mark the coronary arteries by a virtual endoscopic traversal. Virtual endoscopy enables a significantly easier visual identification of the blood vessels in comparison to outside views or slice-by-slice examination methods. Furthermore, we use this marking as a scaffold for the actual segmentation of the coronary arteries.

Abstract. We present a multi-scale approach for non-rigid image registration of tubular tree-like structures such as vessels. Therefore, we consider a Gaussian and a combined morphological and Gaussian scalespace. Similar to a multi-level framework, we solve a sequence of registration problems on several scale-space levels using a standard variational approach for non-liner volumetric image registration. The overall idea is to avoid locally ambiguous mappings between parts of the images by removing morphological details but also finding a global optimal solution by spreading remaining local information using Gaussian scaling. We successfully tested our method on registration of 3D CT and ultrasound images of hepatic vessels. 1

diverging color map. Abstract — Heart disease is the number one killer in the United States, and finding indicators of the disease at an early stage is critical for treatment and prevention. In this paper we evaluate visualization techniques that enable the diagnosis of coronary artery disease. A key physical quantity of medical interest is endothelial shear stress (ESS). Low ESS has been associated with sites of lesion formation and rapid progression of disease in the coronary arteries. Having effective visualizations of a patient’s ESS data is vital for the quick and thorough non-invasive evaluation by a cardiologist. We present a task taxonomy for hemodynamics based on a formative user study with domain experts. Based on the results of this study we developed HemoVis, an interactive visualization application for heart disease diagnosis that uses a novel 2D tree diagram representation of coronary artery trees. We present the results of a formal quantitative user study with domain experts that evaluates the effect of 2D versus 3D artery representations and of color maps on identifying regions of low ESS. We show statistically significant results demonstrating that our 2D visualizations are more accurate and efficient than 3D representations, and that a perceptually appropriate color map leads to fewer diagnostic mistakes than a rainbow color map. Index Terms—Quantitative evaluation, qualitative evaluation, biomedical and medical visualization. 1