Xuebin Chi

Efficient Volume Exploration Using the Gaussian Mixture Model

The multidimensional transfer function is a flexible and effective tool for exploring volume data. However, designing an appropriate transfer function is a trial-and-error process and remains a challenge. In this paper, we propose a novel volume exploration scheme that explores volumetric structures in the feature space by modeling the space using the Gaussian mixture model (GMM). Our new approach has three distinctive advantages. First, an initial feature separation can be automatically achieved through GMM estimation. Second, the calculated Gaussians can be directly mapped to a set of elliptical transfer functions (ETFs), facilitating a fast pre-integrated volume rendering process. Third, an inexperienced user can flexibly manipulate the ETFs with the assistance of a suite of simple widgets, and discover potential features with several interactions. We further extend the GMM-based exploration scheme to time-varying data sets using an incremental GMM estimation algorithm. The algorithm estimates the GMM for one time step by using itself and the GMM generated from its previous steps. Sequentially applying the incremental algorithm to all time steps in a selected time interval yields a preliminary classification for each time step. In addition, the computed ETFs can be freely adjusted. The adjustments are then automatically propagated to other time steps. In this way, coherent user-guided exploration of a given time interval is achieved. Our GPU implementation demonstrates interactive performance and good scalability. The effectiveness of our approach is verified on several data sets.The multidimensional transfer function is a flexible and effective tool for exploring volume data. However, designing an appropriate transfer function is a trial-and-error process and remains a challenge. In this paper, we propose a novel volume exploration scheme that explores volumetric structures in the feature space by modeling the space using the Gaussian mixture model (GMM). Our new approach has three distinctive advantages. First, an initial feature separation can be automatically achieved through GMM estimation. Second, the calculated Gaussians can be directly mapped to a set of elliptical transfer functions (ETFs), facilitating a fast pre-integrated volume rendering process. Third, an inexperienced user can flexibly manipulate the ETFs with the assistance of a suite of simple widgets, and discover potential features with several interactions. We further extend the GMM-based exploration scheme to time-varying data sets using an incremental GMM estimation algorithm. The algorithm estimates the GMM for one time step by using itself and the GMM generated from its previous steps. Sequentially applying the incremental algorithm to all time steps in a selected time interval yields a preliminary classification for each time step. In addition, the computed ETFs can be freely adjusted. The adjustments are then automatically propagated to other time steps. In this way, coherent user-guided exploration of a given time interval is achieved. Our GPU implementation demonstrates interactive performance and good scalability. The effectiveness of our approach is verified on several data sets.

Efficient opacity specification based on feature visibilities in direct volume rendering

Due to 3D occlusion, the specification of proper opacities in direct volume rendering is a time‐consuming and unintuitive process. The visibility histograms introduced by Correa and Ma reflect the effect of occlusion by measuring the influence of each sample in the histogram to the rendered image. However, the visibility is defined on individual samples, while volume exploration focuses on conveying the spatial relationships between features. Moreover, the high computational cost and large memory requirement limits its application in multi‐dimensional transfer function design.

Automating Transfer Function Design with Valley Cell-Based Clustering of 2D Density Plots

Two‐dimensional transfer functions are an effective and well‐accepted tool in volume classification. The design of them mostly depends on the users experience and thus remains a challenge. Therefore, we present an approach in this paper to automate the transfer function design based on 2D density plots. By exploiting their smoothness, we adopted the Morse theory to automatically decompose the feature space into a set of valley cells. We design a simplification process based on cell separability to eliminate cells which are mainly caused by noise in the original volume data. Boundary persistence is first introduced to measure the separability between adjacent cells and to suitably merge them. Afterward, a reasonable classification result is achieved where each cell represents a potential feature in the volume data. This classification procedure is automatic and facilitates an arbitrary number and shape of features in the feature space. The opacity of each feature is determined by its persistence and size. To further incorporate the users prior knowledge, a hierarchical feature representation is created by successive merging of the cells. With this representation, the user is allowed to merge or split features of interest and set opacity and color freely. Experiments on various volumetric data sets demonstrate the effectiveness and usefulness of our approach in transfer function generation.

Volume exploration using ellipsoidal Gaussian transfer functions

This paper presents an interactive transfer function design tool based on ellipsoidal Gaussian transfer functions (ETFs). Our approach explores volumetric features in the statistical space by modeling the space using the Gaussian mixture model (GMM) with a small number of Gaussians to maximize the likelihood of feature separation. Instant visual feedback is possible by mapping these Gaussians to ETFs and analytically integrating these ETFs in the context of the pre-integrated volume rendering process. A suite of intuitive control widgets is designed to offer automatic transfer function generation and flexible manipulations, allowing an inexperienced user to easily explore undiscovered features with several simple interactions. Our GPU implementation demonstrates interactive performance and plausible scalability which compare favorably with existing solutions. The effectiveness of our approach has been verified on several datasets.

Visual Detection of Anomalies in DNS Query Log Data

DNS (Domain Name System) is an essential component of the functionality of the Internet, which converts domain names to the IP addresses. The security of DNS is related to the whole Internet. DNS query log file provide the insights of the DNS security. In this paper we propose an interactive visual analysis system for the DNS log files to intuitively detect the anomalies in DNS query logs. With a theme river based ranking visualization linked with Heat-Dial-map and tree map, user could easy identify anomalies and then further analyze regional and temporal features to help the administrators figure out the reason. Moreover, the features of DNS queries in time and region could also be analysis with this system.

Skeletonization of Grayscale Volumes for Shape Description

Skeletons as important shape features of an object are useful for shape description. Unfortunately, methods obtaining skeletons of a grayscale volume are lacking due to no clear boundary between object and background. In this paper, we present a new segmentation-free skeletonization method on grayscale volumes based on Marching Cubes, topological thinning and a novel pruning routine. Our method is capable of generating a family of skeletal curves and surfaces that lie centered at rod-like and plate-like parts in the grayscale volume for shape description. It is demonstrated on biomedical datasets.

Marching Cube Based Marker-Controlled Watershed Segmentation for CryoEM Density Map

Cryo-electron microscopy (CryoEM) is a very important method for studying the structures of macromolecules. Segmentation is one of the key problems in CryoEM technique. We propose a new 3D watershed, where marching cube is employed as a marker method to control the segmentation. It can transform the domain knowledge into watershed by an interactive interface with isosurface, by which we avoid over-segmentation and obtain the correct segmentation results. Lastly we illustrate the efficiency of the algorithm by testing it on two typical CryoEM density maps.

Interference microscopy volume illustration for biomedical data

In this paper, we propose a novel volume illustration technique inspired by interference microscopy, which has been successfully used in biological, medical and material science over decades. Our approach simulates the optical phenomenon in interference microscopy that accounts light interference over transparent specimens, in order to generate contrast enhanced and illustrative volume visualization results. Specifically, we propose PCVR (Phase- Contrast Volume Rendering) and DICVR (Differential Interference Contrast Volume Rendering) corresponding to Phase-Contrast microscopy and Differential Interference Contrast (DIC) microscopy respectively. Without complex transfer function design, our proposed method can enhance the image contrast and structure details according to the subtle change of Optical Path Differences (OPD), and illustrate the thickness change and occluded structures with interferometry metaphors. In addition, we also develop a user interface to enable slicing specimen sections in volume data. Focus+ context lens are also included in the system for convenient data navigation and exploration. As the proposed methods are based upon widely applied microscopy techniques, they are intuitive for domain experts to explore and analyze the volume data with the proposed methods. The feedbacks from domain users suggest our proposed techniques are useful volume visualization approaches complimentary to the traditional ones.

Symmetry based fast marching method for icosahedral virus segmentation

Segmentation of icosahedral virus density map from cryo-electron microscope (CryoEM) is a challenging task because virus structure is complex and density map is at low resolution. Fast marching method is widely used in segmentation, in which seed selection is essential for correct segmentation results. However, the selection of an appropriate seed is difficult. In this paper, we present the method of selecting the seed in fast marching algorithm by making use of the shape symmetry to improve the fast marching method for icosahedral virus segmentation. Based on the feature of icosahedron, we compute and get its symmetry axes inside the density map. With these symmetry axes, we specify the initial seeds with the local maxima value along symmetry axes. Further, the new data structures are presented, which can effectively reduce the memory cost when implement the fast marching algorithm. Experimental results show that the approach can obtain segmentation results of the density maps fast and accurately.

A Parallel Refined Block Arnoldi Algorithm for Large Unsymmetric Matrices

This paper proposed a parallel refined block Arnoldi method for computing a few eigenvalues with largest or smallest real parts. The method accelerated by Chebyshev iteration is also investigated. We report some numerical results and compare the parallel refined block methods with single vector counterparts. The results show that the proposed method is more efficient than single vector counterparts.