A new approach for view-dependent isosurfacing on volumetric data is described. The approach is designed for client-server environments where the client's computational capabilities are much more limited than those of the server and where the network between the two features bandwidth limits, for example 802.11b wireless. Regions of the dataset that contain no visible part of the isosurface are determined on the server, using an approximate isosurface silhouette and octree-driven processing. The visible regions of interest in the dataset are then transferred to the client for isosurfacing. The approach also enables fast generation of renderings when the viewpoint changes via minimal additional data transfer to the client. Experimental results for application of the approach to volumetric data are also presented here.
The suitability of three classes of strategies governing color combination selection in isosurface-based volume
visualization are explored. These classes are use of: (1) harmonious color combinations, (2) disharmonious color
combinations, and (3) opponent color combinations. Suitability is assessed here via user evaluation of renderings of
isosurfaces with multiple (nested) components. The significance of these user evaluations are also analyzed statistically.
The effect of harmonious versus non-harmonious color combinations in one aspect of information visualization
effectiveness is considered. One focus is the relative suitability of two competing paradigms for determining color combinations
that are harmonious. A second focus is the suitability of using opposing (i.e., opponent) colors for feature
presentation in information visualization. The effects are considered for color item overlays on crowded and noncrowded
displays. A statistical analysis of human responses is also presented.
New image data mining mechanisms that enable content-based
retrieval of images of interest from a large collection of
satellite data of the Earth's aurorae are described. The
mechanisms enable mining for wide aurorae, aurorae with large
ovals, standard aurorae, and theta aurorae. Shape characteristics
of these classes of aurorae are exploited by the mining
mechanisms. Experimental results that benchmark the accuracy of
the mechanisms are also presented.
We introduce a new method that allows the kidney to be extracted (i.e., segmented) from lower torso computerized tomography (CT) datasets. The method combines active contour (snake)-, intensity-, and shape-based processing to extract the kidney. Initially, a general-purpose coarse mathematical shape model of the kidney is extracted by the method. This coarse segmentation is then refined by snake-based deformation.
KEYWORDS: Visualization, Software development, Network on a chip, Computer aided design, 3D modeling, Data modeling, Content addressable memory, Information visualization, Human-machine interfaces, Software engineering
Proc. SPIE. 3960, Visual Data Exploration and Analysis VII
KEYWORDS: Human-machine interfaces, Visualization, 3D modeling, Software development, Software engineering, Computer aided design, 3D visualizations, Solid modeling, Network on a chip, Information visualization
Software visualization is a graphical representation of software characteristics and behavior. Certain modes of software visualization can be useful in isolating problems and identifying unanticipated behavior. In this paper we present a new approach to aid understanding of object- oriented software through 3D visualization of software metrics that can be extracted from the design phase of software development. The focus of the paper is a metric extraction method and a new collection of glyphs for multi- dimensional metric visualization. Our approach utilize the extensibility interface of a popular CASE tool to access and automatically extract the metrics from Unified Modeling Language class diagrams. Following the extraction of the design metrics, 3D visualization of these metrics are generated for each class in the design, utilizing intuitively meaningful 3D glyphs that are representative of the ensemble of metrics. Extraction and visualization of design metrics can aid software developers in the early study and understanding of design complexity.
In this paper, a new local registration method is introduced. The method enables regional refinements to an initial coarse global alignment of two magnetic resonance angiography (MRA) volume datasets through the use of snake-based local elastic deformations on blood vessel curves. The curves are deformed based on corresponding salient features (points of bifurcation and high curvature) that are extracted using a multi-stage method. The extraction involves first tracing blood vessel curves from depth-enhanced maximal intensity projections of the original volume data and then fitting B-splines to the traced structures. The framework for a new approach to volume warping which completes the refinement process for all points in the datasets is also introduced. The local registration method offers the promise registering data collected over time from a patient.
A new multi-stage technique is presented for segmentation of targets of interest in synthetic aperture radar (SAR) data. The method creates an initial coarse segmentation using a histogram-based approach that labels each pixel as foreground or background. The extents of targets of interest are then determined using a hierarchical clustering stage that utilizes a novel weighting of intensity and pixel position. Finally, each potential target's segmentation is improved using probabilistic relaxation labeling. The approach loosens the typical region-based segmentation paradigm that only contiguous pixels can compose a segment. The technique is useful both for target segmentation and as a pre-processing step to verify the fidelity of artificially-generated data with real data.
A new approach is presented for determining left ventricular (LV) shape in a sequence of gated blood pool single photon emission computed tomography data. The approach consists of the automatic extraction and fitting of an analytical ellipsoidal surface that well-approximates the shape of the LV. Data from all slices and geometric characteristics of the analytical surface are exploited to enable robust and (over- constrained) recovery of the LV shape parameters. Examination of 3D renderings of the surface coupled with computation of LV volume and motion parameters over the systolic cycle can allow qualitative and quantitative determination of LV function.
This paper introduces techniques for the extraction of anatomical structures from magnetic resonance (MR) images of the head. The goal of the work is to extract features that are useful for registration of different modalities of tomographic datasets of a patient. These features must therefore be present in multiple modalities of the datasets. Three such features that can be extracted are the location of the eyes, the longitudinal fissure, and the lateral ventricles. In this paper, we present our methods for extracting these features. The techniques exploit geometric shape characteristics to aid in the extraction process, chiefly through the use of Hough-based accumulations for location of the eyes and longitudinal fissure. An approach consisting of volume-growing followed by a locally adaptive histogramming is used for extraction of the ventricles.
This paper describes a suite of high performance medical visualization tools implemented using a networked computing configuration. The tools are designed to supply interactive and near-real-time visualization capability for volumetric data to assist in diagnosis, monitoring, and surgical planning for kidney disorders, especially the Von Hippel Lindau Syndrome. The networked configuration combines the computing power of a vector-parallel supercomputer with the interactive graphics capability of a high-end workstation. In this paper, our focus is on the image rendering and interactive data exploration functional units of the system. One computationally intensive feature extraction and image rendering function - including the marching cubes, volume ray casting, and surface ray tracing - have been vectorized and are discussed. We also present interactive exploration tools for viewing arbitrary orthogonal sets of planes and a probing tool for 3D measurements. These latter tools were implemented on the workstation.
We present an efficient technique that uses bidirectional template matching for gross defect detection in 3D objects. The template matching utilizes computer-aided design models and is carried out on 3D data formed by merging range data collected from multiple known viewpoints. The inspection scheme assumes that the identity of the object is known but that the object's position is unknown, although constrained. A matching technique based on the Hausdorff distance is used to determine the pose of the object. The inspection scheme is tested on real range images of several iron castings.
A suite of model-driven techniques for identification of 3-D quadric surfaces (cones, cylinders, and spheres) in segmented range imagery is presented. These techniques use range data, surface normal calculated on that data, knowledge of geometric characteristics of the various surfaces, and known model parameters to perform the classification. Second derivative quantities such as curvature, which are unreliable in the presence of noise, are avoided. Model information such as radii and vertex angles are used to guide the classification. Hough-based techniques are employed for extraction of spherical and cylindrical parameters, while conic parameters are presented for numerous scenes of both real and synthetic objects including part jumbles, objects in many poses, and noiseless and noisy synthetic objects. Empirical tests reveal that these methods have advantages (e.g. they appear to be very accurate) over previous methods.