Techniques and software architectures for medical visualisation and image processing

This thesis presents a flexible software platform for medical visualisation and image processing, a technique for the segmentation of the shoulder skeleton from CT data and three techniques that make contributions to the field of direct volume rendering. Our primary goal was to investigate the use of visualisation techniques to assist the shoulder replacement process. This motivated the need for a flexible environment within which to test and develop new visualisation and also image processing techniques with a medical focus. The Delft Visualisation and Image processing Development Environment, or DeVIDE, was created to answer this need. DeVIDE is a graphical data-flow application builder that combines visualisation and image processing techniques, supports the rapid creation of new functional components and facilitates a level of interaction with algorithm code and parameters that differentiates it from similar platforms. For visualisation, measurement and pre-operative planning, an accurate segmentation from CT data of the bony structures of the shoulder is required. Due to the complexity of the shoulder joint and the fact that a method was required that could deal with diseased shoulders, existing techniques could not be applied. In this thesis we present a suite of techniques for the segmentation of the skeletal structures from CT data, especially designed to cope with diseased shoulders. Direct volume rendering, or DVR, is a useful visualisation technique that is often applied as part of medical visualisation solutions. A crucial component of an effective DVR visualisation is a suitable transfer function that assigns optical characteristics to the data. Finding a suitable transfer function is a challenging task. We present two highly interactive methods that facilitate this process. We also present a method for interactive direct volume rendering on ubiquitous low-end graphics hardware. This method, called ShellSplatting, is optimised for the rendering of bony structures from CT data and supports the hardware-assisted blending of traditional surface rendering and direct volume rendering. This characteristic is useful in surgical simulation applications. ShellSplatting is based on the object-order splatting of discrete voxels. As such, maintaining a correct back-to-front or front-to-back ordering during rendering is crucial for correct images. All existing real-time perspective projection visibility orderings show artefacts when splatting discrete voxels. We present a new ordering for perspective projection that remedies these artefacts without a noticeable performance penalty.Electrical Engineering, Mathematics and Computer Scienc

Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy

Purpose Automated patient-specific image-based segmentation of tissues surrounding aseptically loose hip prostheses is desired. For this we present an automated segmentation pipeline that labels periprosthetic tissues in computed tomography (CT). The intended application of this pipeline is in pre-operative planning. Methods Individual voxels were classified based on a set of automatically extracted image features. Minimum-cost graph cuts were computed on the classification results. The graph-cut step enabled us to enforce geometrical containment constraints, such as cortical bone sheathing the femur’s interior. The solution’s novelty lies in the combination of voxel classification with multilabel graph cuts and in the way label costs were defined to enforce containment constraints. Results The segmentation pipeline was tested on a set of twelve manually segmented clinical CT volumes. The distribution of healthy tissue and bone cement was automatically determined with sensitivities greater than 82% and pathological fibrous interface tissue with a sensitivity exceeding 73%. Specificity exceeded 96% for all tissues. Conclusions The addition of a graph-cut step improved segmentation compared to voxel classification alone. The pipeline described in this paper represents a practical approach to segmenting multitissue regions from CT.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets

Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis’04 contest), the ionization front instability data set (Vis’08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

L'Europe: Le nouvel espace public

Paper presented at First Meeting of the Specialist Study Group ‘Media and Communication in Europe’ at the University of Leeds, EU and the public sphere(s), 17-18 November 2006No publicad

Efficient seeding and defragmentation of curvature streamlines for colonic polyp detection

Many computer aided diagnosis (CAD) schemes have been developed for colon cancer detection using Virtual Colonoscopy (VC). In earlier work, we developed an automatic polyp detection method integrating flow visualization techniques, that forms part of the CAD functionality of an existing Virtual Colonoscopy pipeline. Curvature streamlines were used to characterize polyp surface shape. Features derived from curvature streamlines correlated highly with true polyp detections. During testing with a large number of patient data sets, we found that the correlation between streamline features and true polyps could be affected by noise and our streamline generation technique. The seeding and spacing constraints and CT noise could lead to streamline fragmentation, which reduced the discriminating power of our streamline features. In this paper, we present two major improvements of our curvature streamline generation. First, we adapted our streamline seeding strategy to the local surface properties and made the streamline generation faster. It generates a significantly smaller number of seeds but still results in a comparable and suitable streamline distribution. Second, based on our observation that longer streamlines are better surface shape descriptors, we improved our streamline tracing algorithm to produce longer streamlines. Our improved techniques are more efficient and also guide the streamline geometry to correspond better to colonic surface shape. These two adaptations support a robust and high correlation between our streamline features and true positive detections and lead to better polyp detection results.Electrical Engineering, Mathematics and Computer Scienc

Interactive visualization of fused fMRI and DTI for planning brain tumor resections

The surgical removal of brain tumors can lead to functional impairment. Therefore it is crucial to minimize the damage to important functional areas during surgery. These areas can be mapped before surgery by using functional MRI. However, functional impairment is not only caused by damage to these areas themselves. It is also caused by damage to the fiber bundles that connect these areas with the rest of the brain. Diffusion Tensor Images (DTI) can add information about these connecting fiber bundles. In this paper we present interactive visualization techniques that combine DTI, fMRI and structural MRI to assist the planning of brain tumor surgery. Using a fusion of these datasets, we can extract the fiber bundles that pass through an offset region around the tumor, as can be seen in Figure 1. These bundles can then be explored by filtering on distance to the tumor, or by selecting a specific functional area. This approach enables the surgeon to combine all this information in a highly interactive environment in order to explore the pre-operative situation.Data Visualization GroupElectrical Engineering, Mathematics and Computer Scienc

Smooth Graphs for Visual Exploration of Higher-Order State Transitions

In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the timeseries is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Articulated Planar Reformation for Change Visualization in Small Animal Imaging

The analysis of multi-timepoint whole-body small animal CT data is greatly complicated by the varying posture of the subject at different timepoints. Due to these variations, correctly relating and comparing corresponding regions of interest is challenging. In addition, occlusion may prevent effective visualization of these regions of interest. To address these problems, we have developed a method that fully automatically maps the data to a standardized layout of sub-volumes, based on an articulated atlas registration. We have dubbed this process articulated planar reformation, or APR. A sub-volume can be interactively selected for closer inspection and can be compared with the corresponding sub-volume at the other timepoints, employing a number of different comparative visualization approaches. We provide an additional tool that highlights possibly interesting areas based on the change of bone density between timepoints. Furthermore we allow visualization of the local registration error, to give an indication of the accuracy of the registration. We have evaluated our approach on a case that exhibits cancer-induced bone resorption.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Interactive local super-resolution reconstruction of whole-body MRI mouse data: A pilot study with applications to bone and kidney metastases

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.ImPhys/Imaging PhysicsApplied Science