Evaluation of artery visualizations for heart disease diagnosis

Heart disease is the number one killer in the United States, and finding indicators of the disease at an early stage is critical for treatment and prevention. In this paper we evaluate visualization techniques that enable the diagnosis of coronary artery disease. A key physical quantity of medical interest is endothelial shear stress (ESS). Low ESS has been associated with sites of lesion formation and rapid progression of disease in the coronary arteries. Having effective visualizations of a patient's ESS data is vital for the quick and thorough non-invasive evaluation by a cardiologist. We present a task taxonomy for hemodynamics based on a formative user study with domain experts. Based on the results of this study we developed HemoVis, an interactive visualization application for heart disease diagnosis that uses a novel 2D tree diagram representation of coronary artery trees. We present the results of a formal quantitative user study with domain experts that evaluates the effect of 2D versus 3D artery representations and of color maps on identifying regions of low ESS. We show statistically significant results demonstrating that our 2D visualizations are more accurate and efficient than 3D representations, and that a perceptually appropriate color map leads to fewer diagnostic mistakes than a rainbow color map.     

An investigation of student practices in asynchronous computer conferencing courses

This study investigated the online practices of students enrolled in graduate-level distance education courses. Using interviews and a questionnaire as data sources, the study sought to: (a) identify common practices that students adopt in asynchronous discussions, and (b) gain an understanding of why students adopt them. An analysis of the data suggests that many of the practices are coping mechanisms developed to help students more easily meet course participation requirements. Some of these are time saving strategies designed to reduce information overload (e.g., skimming messages rather than reading them carefully). Other strategies are designed to help students project an image of themselves as knowledgeable and collaborative course participants. It is argued that although these practices provide students with a level of efficiency in terms of meeting course requirements, they may inadvertently undermine learning.     

Evaluating the relationship between chemical exposure and cryptorchidism

One method for the evaluation of complex environmental and health datasets is the discrete mathematical method Hasse diagram technique based on partial orders. The introduced software program package is named PyHasse. In this paper we evaluate a possible human association between maternal exposure to organochlorine compounds used as pesticides and cryptorchidism among male children in Finland and Denmark. We identified differences in comparable and incomparable objects and quantified these differences by the software tool Similarity Analysis in the program PyHasse. Furthermore we interpreted the corresponding Hasse diagrams concerning chosen “striking objects”. We found the position of the chemicals AHCH (alpha-Hexachlorohexane), CHCE (cis-Heptachloroepoxide), DIEL (Dieldrin), and MIRE (Mirex) has some influence on the differentiation of the Hasse diagrams and hence of each two datasets analyzed. The largest disparities can be observed when we compare the Finnish and Danish datasets concerning cryptorchidism. The disparities are demonstrated in the corresponding Hasse diagrams.     

Adjustable speed surface subdivision

We introduce a non-uniform subdivision algorithm that partitions the neighborhood of an extraordinary point in the ratio σ:1−σ, where σ(0,1). We call σ the speed of the non-uniform subdivision and verify C¹ continuity of the limit surface. For σ=1/2, the Catmull–Clark limit surface is recovered. Other speeds are useful to vary the relative width of the polynomial spline rings generated from extraordinary nodes.     

A system for efficient motor learning using multimodal augmented feedback

Numerous studies have established that using various forms of augmented feedback improves human motor learning. In this paper, we present a system that enables real-time analysis of motion patterns and provides users with objective information on their performance of an executed set of motions. This information can be used to identify individual segments of improper motion early in the learning process, thus preventing improperly learned motion patterns that can be difficult to correct once fully learned. The primary purpose of the proposed system is to serve as a general tool in the research on impact of different feedback modalities on the process of motor learning, for example, in sports or rehabilitation. The key advantages of the system are high-speed and high-accuracy tracking, as well as its flexibility, as it supports various types of feedback (auditory and visual, concurrent or terminal). The practical application of the proposed system is demonstrated through the example of learning a golf swing.     

Rapid scalar value classification and volume clipping for?interactive 3D medical image visualization

In many clinical scenarios, medical data visualization and interaction are important to physicians for exploring inner anatomical structures and extracting meaningful diagnostic information. Real-time high-quality volume rendering, artifact-free clipping, and rapid scalar value classification are important techniques employed in this process. Unfortunately, in practice, it is still difficult to achieve an optimal balance. In this paper, we present some strategies to address this issue, which are based on the calculation of segment-based post color attenuation and dynamic ray–plane intersection (RPI) respectively. When implemented within our visualization system, the new classification algorithm can deliver real-time performance while avoiding the “color over-accumulation” artifacts suffered by the commonly used acceleration algorithms that employ pre-integrated classification. Our new strategy can achieve an optimized balance between image quality and classification speed. Next, the RPI algorithm is used with opacity adjustment technique to effectively remove the “striping” artifacts on the clipping plane caused by the nonuniform integration length. Furthermore, we present techniques for multiple transfer function (TF) based anatomical feature enhancement and “keyhole” based endoscopic inner structure view. Finally, the algorithms are evaluated subjectively by radiologists and quantitatively compared using image power spectrum analysis.     

On the Use of Enumeration for Investigating the Performance of Hypothesis Tests for Economic Models with a Discrete Response Variable

This article notes that it is now practical to use the method of enumerationto analyse the performance of estimators and hypothesis tests of fullyparametric binary data models. The general method is presented and thenemployed to investigate the power performance of a common misspecificationtest for the Probit model. The advantages, disadvantages and limitations ofenumeration compared with standard Monte Carlo simulation are thendiscussed. Finally, an example from experimental economics is used todemonstrate that the methodology can also be used in small empirical studies.     

Rough Neural Computing in Signal Analysis

This paper introduces an application of a particular form of rough neural computing in signal analysis. The form of rough neural network used in this study is based on rough sets, rough membership functions, and decision rules. Two forms of neurons are found in such a network: rough membership function neurons and decider neurons. Each rough membership function neuron constructs upper and lower approximation equivalence classes in response to input signals as an aid to classifying inputs. In this paper, the output of a rough membership function neuron results from the computation performed by a rough membership function in determining degree of overlap between an upper approximation set representing approximate knowledge about inputs and a set of measurements representing certain knowledge about a particular class of objects. Decider neurons implement granules derived from decision rules extracted from data sets using rough set theory. A decider neuron instantiates approximate reasoning in assessing rough membership function values gleaned from input data. An introduction to the basic concepts underlying rough membership neural networks is briefly given. An application of rough neural computing in classifying the power system faults is considered.