3D-LICに基づくベクタ場の領域制限可視化手法

In order to reflect the internal motion characteristics of entire vector fields, texture visualization methods based on line integral convolution are usually adopted. However, the visualization results obtained in this way have low image quality. To solve this problem, this paper suggests using line integral convolution to optimize two specific aspects - texture enhancement and color enhancement – to provide an enhanced vector field visualization model. Existing texture enhancement algorithms can create texture aliasing. Based on an analysis of the relationship between vector angles, sampling distance and texture aliasing, the paper puts forward a texture enhancement algorithm that uses the vector angle to adjust the sampling distance of a high-pass filter. This greatly reduces the presence of texture aliasing. For color enhancement, a linear algorithm is usually used that adds vector size information to the vector field. However, the resulting image has a problem of color concentration. In view of this, the distribution characteristics of the vector field are analyzed using a histogram and a dynamic nonlinear color enhancement algorithm is proposed. This noticeably improves the color distribution of the resulting image and improves the overall visual quality of the result.

Region-of-interest visualization by CAVE VR system with automatic control of level-of-detail

A visualizing application of line integral convolution techniques

We address the problem of tracking points in dense vector fields. Such vector fields may come from computational fluid dynamics simulations, environmental monitoring sensors, or dense point tracking of video data. To track points in vector fields, we capture the distribution of higher-order properties (e.g., properties derived from the gradient of the velocity vector field) in a novel local descriptor called a vector spin-image. Our distribution-based approach has a number of advantages over methods that use topology analysis to track points in vector fields. The local distributions are robust to noise, adaptable to changes in the feature, and can be used to extrapolate the location of features after they have disappeared. We describe the vector spin-image data structure, the higher-order properties we record to track vector field points, and show results of tracking points in the simulated flow through a diesel engine cylinder.

Line Integral Convolution (LIC) is a common approach for the visualisation of 2D vector fields. It is well suited for visualizing the direction of a flow field, but it gives no information about the orientation of the underlying vectors. We introduce Oriented Line Integral Convolution (OLIC), where direction as well as orientation are encoded within the resulting image. This is achieved by using a sparse input texture and a ramp like (anisotropic) convolution kernel. This method can be used for animations, whereby the computation of so called pixel traces fastens the calculation process. Various OLICs illustrating simple and real world vector fields are shown.

The need for efficient seismic interference (SI) attenuation solutions is essential to reduce time-sharing that is very costly. We present two SI attenuation methods, both based on the automatic vector field estimation of the SI local moveout. The first method proposes an automatic determination of tau-p mute parameters, while the second one uses line integral convolution and the estimated vector field to separate SI from reflection hyperbolas. Because of the automatic detection of SI attributes, they require little user interaction and the processing time is short. Both methods are tested on real shot gathers and successfully attenuate the SI while preserving the reflections.

Line integral convolution (LIC) is an effective technique for visualizing vector fields. The application of LIC to 3D flow fields has yet been limited by difficulties to efficiently display and animate the resulting 3D-images. Texture-based volume rendering allows interactive visualization and manipulation of 3D-LIC textures. In order to ensure the comprehensive and convenient exploration of flow fields, we suggest interactive functionality including transfer functions and different clipping mechanisms. Thereby, we efficiently substitute the calculation of LIC based on sparse noise textures and show the convenient visual access of interior structures. Further on, we introduce two approaches for animating static 3D-flow fields without the computational expense and the immense memory requirements for pre-computed 3D-textures and without loss of interactivity. This is achieved by using a single 3D-LIC texture and a set of time surfaces as clipping geometries. In our first approach we use the clipping geometry to pre-compute a special 3D-LIC texture that can be animated by time-dependent color tables. Our second approach uses time volumes to actually clip the 3D-LIC volume interactively during rasterization. Additionally, several examples demonstrate the value of our strategy in practice.

Line Integral Convolution (LIC) has been widely adopted in pencil hatching generation, where an image degraded by random binary white noise (RBWN) is filtered by LIC along a priori vector field. Nonetheless, an RBWN degraded image through LIC produces hatching graduation only in terms of stroke intensity, and can neither produce hatching graduation explicitly in stroke density nor create visually clear hatching strokes while input pixel values are fairly low. In this paper we address these issues from a noise point of view by assessing several noise models and subsequently constructing a new noise model, called hybrid noise. The new noise model has been experimentally demonstrated in simulating hatching graduation in terms of both stroke intensity and stroke density with quantified graduality measurements. To oversee the effectiveness of hybrid noise we implement the whole pipeline of pencil drawing simulation and compare our results with those state of the art algorithms.

Purpose: Accurate Deformable Image Registration (DIR) algorithms are essential to clinical implementation of adaptive planning strategies hence finding validation strategies for DIR algorithms remains a pressing concern. Most validation efforts are based on contour or landmark tracking, thus sampling the Deformable Vector Field (DVF) relatively sparsely. The primary purpose of this work is to assess interchangeability of DIR algorithms in dose accumulation, and assess if contour based methods are sufficient to validate the equivalence of DIR algorithms. Methods: We registered peak inhale and peak exhale phases of thirteen lung patients using three DIR algorithms. The DVF maps were pairwise compared through voxel‐by‐voxel subtraction of vector fields. The vector difference maps were analyzed by building volume histograms on regions of interest. This method of analysis is directly relevant to the Dose Volume Histogram accumulation, as vector difference between the maps will be translated into a distance between dose interpolation points. We further compared Jacobian distributions for the three maps, as local derivatives of DVF maps would be important to any algorithm that attempts local density corrections. We performed contour based comparison of the three algorithms, to connect this validation method to prior work. Results: The volume histogram analysis shows that differences between DVFs in 2% tails of volume histogram are in the 1cm – 4cm range, although the contour‐based analysis using Diceˈs Similarity Coefficient (DSC) would suggest that the three algorithms are nearly equivalent. For most structures, spatial differences between maps are below 0.5cm over approximately 70% of structure volume, and exceed 0.5cm over the remainder. Jacobian distributions differ significantly, implying that local density corrections are strongly algorithm dependent. Conclusions: Differences between algorithms are potentially significant for dose accumulation, and such differences are not revealed by contour based comparisons.Acknowledgments: Supported by NIH Grant P01 CA11602

Chapter 5 - Vorticity Dynamics

Vector fields may come from video data (via optical flow and tracking), from weather phenomena (e.g., wind speed and direction), and from medical imaging. An important component in analyzing this data is to be able to quantitatively compare different points within a vector field or across different fields of the same type. We present a novel local descriptor to compare individual points in a vector field and rank them based on which are most similar to a selected point. The descriptor captures the statistics of the neighboring vector field around a central point and is discriminating, robust to noise, and efficient to generate and compare.

On the existence and uniqueness of connecting orbits

Calpains are Ca(2+)-dependent cysteine proteases known to be important for the regulation of cell functions and which aberrant activation causes cell death in a number of degenerative disorders. To provide a tool for monitoring the status of calpain activity in vivo under physiological and pathological conditions, we created a mouse model that expresses ubiquitously a fluorescent reporter consisting of eCFP and eYFP separated by a linker cleavable by the ubiquitous calpains. We named this mouse CAFI for calpain activity monitored by FRET imaging. Our validation studies demonstrated that the level of calpain activity correlates with a decrease in FRET (fluorescence resonance energy transfer) between the two fluorescent proteins. Using this model, we observed a small level of activity after denervation and fasting, a high level of activity during muscle regeneration and ischemia, and local activity in damaged myofibers after exercise. Finally, we crossed the CAFI mouse with the alpha-sarcoglycan-deficient model, demonstrating an increase of calpain activity at the steady state. Altogether, our results present evidence that CAFI mice could be a valuable tool in which to follow calpain activity at physiological levels and in disease states.

Clustering of Medical Images for Analysis: A Fuzzy Approach

Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields. However, due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields, existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution, resulting in incomplete and inaccurate vortex information. To address this, we propose an adaptive Line Integral Convolution (LIC) based geographic flow field visualization method by means of rotation distance. Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively. We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution, with the integration step size adjusted. This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields. Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.