10374803
Hemispherical Harmonic Surface Description and Applications to Medical Image Analysis
The use of surface harmonics for rigid and nonrigid shape description is well known. In this paper we define a set of complete hemispherical harmonic basis functions on a hemisphere domain and propose a novel parametric shape description method to efficiently and flexibly represent the surfaces of anatomical structures in medical images. As the first application of hemispherical harmonic theory in shape description, our technique differs from the previous surface harmonics shape descriptors, all of which don't work efficiently on the hemisphere-like objects that often exist in medical anatomical structures (e.g., ventricles, atriums, etc.). We demonstrate the effectiveness of our approach through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for surface modeling efficiency is described and the comparison results demonstrated that our method outperformed the previous approaches using spherical harmonic models.
33719729
New Algorithms for Efficient Mining of Association Rules
Abstract Discovery of association rules is an important data mining task. Several algorithms have been proposed to solve this problem. Most of them require repeated passes over the database, which incurs huge I/O overhead and high synchronization expense in parallel cases. There are a few algorithms trying to reduce these costs. But they contain weaknesses such as often requiring high pre-processing cost to get a vertical database layout, containing much redundant computation in parallel cases, and so on. We propose new association mining algorithms to overcome the above drawbacks, through minimizing the I/O cost and effectively controlling the computation cost. Experiments on well-known synthetic data show that our algorithms consistently outperform a priori, one of the best algorithms for association mining, by factors ranging from 2 to 4 in most cases. Also, our algorithms are very easy to be parallelized, and we present a parallelization for them based on a shared-nothing architecture. We observe that our parallelization develops the parallelism more sufficiently than two of the best existing parallel algorithms.
6859814
Three-dimensional Models for Cardiac Bioelectricity Simulation: Cell to Organ
Computer models of the heart have become useful tools in improving understanding of cardiac electrical activation at the cellular level, and in analyzing mechanisms of arrhythmias and antiarrhythmic therapies. In this paper, we propose to use spherical harmonic (SPHARM) surface reconstruction and alignment methods to generate curvilinear meshes for finite element models of ventricular anatomy which incorporate detailed structural information. The endocardial and epicardial surfaces are defined by SPHARM, and the volume between these surfaces is divided into nested shells. Consequently, the resulting mesh comprises hexahedral elements. Using this novel SPHARM-based meshing method, the transmembrane potential propagation is simulated, based on FitzHugh—Nagumo reaction—diffusion equations. The dynamic electrical activation propagations are simulated on realistic cardiac data during a heart cycle. The obtained results clearly demonstrate an accurate resolution of the cardiac potential during the excitation. Our novel curvilinear mesh models have a great potential to be used in an improved simulation of cardiovascular pathologies for testing therapy strategies and planning interventions.
2480556
Data Brokers: Building Collections through Automated Negotiation
Collecting digital materials is time-consuming and can gain from automation. Since each source – and even each acquisition – may involve a separate negotiation of terms, a collector may prefer to use a broker to represent his interests with owners. This paper describes the Data Broker Framework (DBF), which is designed to automate the process of digital object acquisition. For each acquisition, a negotiation agent is assigned to negotiate on the collector’s behalf, choosing from strategies in a strategy pool to automatically handle most bargaining cases and decide what to accept and what counteroffers to propose. We introduce NOODLE (Negotiation OntOlogy Description LanguagE) to formally specify terms in the negotiation domain.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.
15225252
Morphometric Analysis of Hippocampal Shape in Mild Cognitive Impairment: An Imaging Genetics Study
A computational framework is presented for surface based morphometry to localize shape changes between groups of 3D objects. It employs the spherical harmonic (SPHARM) method for surface modeling and random field theory (RFT) for statistical inference. Several new components are introduced to overcome previous limitations: (1) a general linear model is used to facilitate controlling for covariates; (2) a new SPHARM registration method SHREC is proposed to better align SPHARM models; and (3) an estimated smoothness is used in RFT-based analysis to obtain more accurate results. This framework is applied in a mild cognitive impairment (MCI) study to examine hippocampal shape changes related to diagnostic and genetic conditions. Several interesting findings from our analyses suggest combining imaging phenotypes and genetic profiles has the potential to elucidate biological pathways for better understanding MCI and Alzheimer's disease.
11178688
Dynamically utilizing computation accelerators for extensible processors in a software approach
In recent years, it is increasingly common to see using application specific instruction set processors (ASIPs) in embedded system designs. These ASIPs can offer the ability of customizing hardware computation accelerators for an application domain. Along with instruction set extensions (ISEs), the customized accelerators can significantly improve the performance of embedded processors, which has already been exemplified in previous research work and industrial products. However, these accelerators in ASIPs can only accelerate the applications that are compiled with ISEs. Those applications compiled without ISEs can not benefit from the hardware accelerators at all. In this paper, we propose using software dynamic binary translation to overcome this problem, i.e. dynamically utilizing the accelerators. Unlike a static approach, dynamically utilizing accelerator poses many new problems. This paper comprehensively explores the techniques and design choices for solving these problems, and demonstrates the effectiveness by the results of experiments.
35408245
A quick method for synthesizing photorealistic color images under various illumination conditions
We propose a fast method to synthesize realistic images under novel lighting directions using three color images. In the method, we use a look-up table procedure to estimate the surface orientations, instead of complex computations. Moreover, we explain a way to separate the diffuse and specular reflection components from the three color images.
27674470
Application of Software Fault Tree Analysis to an Airport Ground Control System
With the ever increasing role of software in our day-to-day life, and even more important the criticality of their role in safety and reliability of the applications, it is important to find techniques that helps with identifying and balancing risks. One such technique is Fault Tree Analysis, which has proven to be powerful in other domains such as systems and processes. This paper, describes the results of application of the fault tree analysis to software requirement specification for an airport ground control system. In addition, this paper proposes a set of guidelines on how to apply SFTA to software requirement specification.
8221408
RTL concurrent fault simulation
The integrated circuit (IC) design has been pushed to hardware description language (HDL) description and high-level synthesis (HLS) techniques. The IC testing is also going to the high-level one. For the high-level testing, like gate level one, it needs a circuit model at high level such that, The model is easy to be converted and extended from the HDL descriptions. The model should be unified for multiple usages such as fault simulation, test generation, testability measure, etc. For various algorithms, it is easy to perform forward and backward tracing on the model, and easy to define fault models. In this paper, we try to construct such model and apply it to the fault simulation first.
74980
PLS and SVD based penalized logistic regression for cancer classification using microarray data
Accurate cancer prediction is important for treatment of cancers. The combination of two dimension reduction methods, partial least squares (PLS) and singular value decomposition (SVD), with the penalized logistic regression (PLR) has created powerful classifiers for cancer prediction using microarray data. Comparing with support vector machine (SVM) on seven publicly available cancer datasets, the new algorithms can achieve very good performance and run much faster. They also have the advantage that the probabilities of predictions can be directly given. PLS based PLR is also combined with recursive feature elimination (RFE) to select a 16-gene subset for acute leukemia cancer classification. The testing error on this subset of genes is empirically zero.