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As described in the previous sections, post processing of S-CT offers challenging possibilities for Radiology including LTS imaging and assessment. The inherent information of S-CT can be displayed in different ways to the referring clinician in order to facilitate optimal patient management. Modern information technology and affordable computer hardware allow for new ways of data exchange and interaction between hospital departments. By using internet technology, asynchronous, interactive access to results of imaging and post processing can be provided. For imaging of LTS many facettes of the anatomy can be shown in order to provide a road map for therapeutical decisions. Looking at all these possibilities together, it is safe to say that a digital revolution is taking place in Radiology. [ABSTRACT FROM AUTHOR]

Abdominal Aortic Aneurysm (AAA) is a dangerous condition where the weakening of the aortic wall leads to its deformation and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAAs can be treated non-invasively by means of the Endovascular Aneurysm Repair technique (EVAR), which consists of placing a stent-graft inside the aorta in order to exclude the bulge from the blood circulation and usually leads to its contraction. Nevertheless, the bulge may continue to grow without any apparent leak. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm, which is a very time-consuming task. Here we describe the initial results of a novel model-based approach for the semi-automatic segmentation of both the lumen and the thrombus of AAAs, using radial functions constrained by a priori knowledge and spatial coherency. [ABSTRACT FROM AUTHOR]

The extraction of curve skeletons from tubular networks is a necessary prerequisite for virtual endoscopy applications. We present an approach for curve skeleton extraction directly from gray value images that supersedes the need to deal with segmentations and skeletonizations. The approach uses properties of the Gradient Vector Flow to derive a tube-likeliness measure and a medialness measure. Their combination allows the detection of tubular structures and an extraction of their medial curves that stays centered also in cases where the structures are not tubular such as junctions or severe stenoses. We present results on clinical datasets and compare them to curve skeletons derived with different skeletonization approaches from high quality segmentations. Our approach achieves a high centerline accuracy and is computationally efficient by making use of a GPU based implementation of the Gradient Vector Flow. [ABSTRACT FROM AUTHOR]

In the animation, the process of rigging a character is an elaborated and time consuming task. The rig is developed for a specific character, and it can not be reused in other meshes. In this paper we present a method to automatically adjust a human-like character rig to an arbitrary human-like 3D mesh, using a extracted skeleton obtained from the input mesh. Our method is based on the selection and extraction of feature points, to find an equivalence between an extracted skeleton and the animation rig. [ABSTRACT FROM AUTHOR]

This paper presents a two-step algorithm to perform automatic extraction of vessel tree on angiogram. Firstly, the approximate vessel centerline is modeled as marked point process with each point denoting a line segment. A Double Area prior model is proposed to incorporate the geometrical and topological constraints of segments through potentials on the interaction and the type of segments. Data likelihood allows for the vesselness of the points which the segment covers, which is computed through the Hessian matrix of the image convolved with 2-D Gaussian filter at multiple scales. Optimization is realized by simulated annealing scheme using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. Secondly, the extracted approximate vessel centerline, containing global geometry shape as well as location information of vessel, is used as important guide to explore the accurate vessel edges by combination with local gradient information of angiogram. This is implemented by morphological homotopy modification and watershed transform on the original gradient image. Experimental results of clinical digitized coronary angiogram are reported. [ABSTRACT FROM AUTHOR]

In this paper we propose a weakly supervised learning algorithm for appearance models based on the minimum description length (MDL) principle. From a set of training images or volumes depicting examples of an anatomical structure, correspondences for a set of landmarks are established by group-wise registration. The approach does not require any annotation. In contrast to existing methods no assumptions about the topology of the data are made, and the topology can change throughout the data set. Instead of a continuous representation of the volumes or images, only sparse finite sets of interest points are used to represent the examples during optimization. This enables the algorithm to efficiently use distinctive points, and to handle texture variations robustly. In contrast to standard elasticity based deformation constraints the MDL criterion accounts for systematic deformations typical for training sets stemming from medical image data. Experimental results are reported for five different 2D and 3D data sets. [ABSTRACT FROM AUTHOR]

Virtual colon unfolding is a method to produce an unfold image from volume data. The previous method generates only 2D images casting rays from a central path toward the colon surface and maps the color value into 2D regular grid. However, since rays cannot be reached behind folds, some regions are not represented on the final image. In order to represent those areas adequately, we exploit a height field derived from ray casting. Since the problematic areas have high gradient in comparison to neighboring regions, the differences of height value in those areas are relatively large. Therefore, we can find problematic areas using the height field. To visualize those areas, we exploit supersampling method that casts additional rays toward perpendicular direction of the original rays. Experimental results show that our method represents colon folds accurately. [ABSTRACT FROM AUTHOR]

2D-3D registration for angiographic liver interventions is an unsolved problem mainly because of two reasons. First, a suitable protocol for Computed Tomography Angiography (CTA) to contrast liver arteries is not used in clinical practice. Second, an adequate registration algorithm which addresses the difficult task of aligning deformed vessel structures has not been developed yet. We address the first issue by introducing an angiographic CT scanning phase and thus create a strong link between radiologists and interventionalists. The scan visualizes arteries similar to the vasculature captured with an intraoperative C-arm acquiring Digitally Subtracted Angiograms (DSAs). Furthermore, we propose a registration algorithm using the new CT phase that aligns arterial structures in two steps: a) Initialization of one corresponding feature using vessel diameter information, b) optimization on three rotational and one translational parameter to register vessel structures that are represented as centerline graphs. We form a space of good features by iteratively creating new graphs from projected centerline images and by restricting the correspondence search only on branching points (the vertices) of the vessel tree. This algorithm shows good convergence and proves to be robust against deformation changes, which is demonstrated through studies on one phantom and three patients. [ABSTRACT FROM AUTHOR]

A computer-aided diagnosis (CAD) method is reported that allows the objective identification of subjects with connective tissue disorders from 3D aortic MR images using segmentation and independent component analysis (ICA). The first step to extend the model to 4D (3D + time) has also been taken. ICA is an effective tool for connective tissue disease detection in the presence of sparse data using prior knowledge to order the components, and the components can be inspected visually. 3D+time MR image data sets acquired from 31 normal and connective tissue disorder subjects at end-diastole (R-wave peak) and at 45% of the R-R interval were used to evaluate the performance of our method. The automated 3D segmentation result produced accurate aortic surfaces covering the aorta. The CAD method distinguished between normal and connective tissue disorder subjects with a classification accuracy of 93.5 %. [ABSTRACT FROM AUTHOR]

A novel robust active appearance model (AAM) matching algorithm is presented. The method consists of two main stages. First, initial residuals are clustered by a non parametric mean shift mode detection step. Second, modes without gross outliers are selected using an objective function. Robustness of the matching procedure is demonstrated on a variety of examples with different noise conditions. The proposed algorithm outperformed the conventional AAM matching on images with gross disturbances and can tolerate up to 40% of disturbed data. [ABSTRACT FROM AUTHOR]

Spinal sonography provides a detailed panoramic view of the spinal canal and its contents in neonates and young infants [1]. [ABSTRACT FROM AUTHOR]