12931405
An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging
A procedure for estimating the joint probability density function (pdf) of T1, T2 and proton spin density (PD) for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the brain is presented. The pdf's have numerous applications, including the study of tissue parameter variability in pathology and across populations. The procedure requires a multispectral, spin echo magnetic resonance imaging (MRI) data set of the brain. It consists of five automated steps: (i) preprocess the data to remove extracranial tissue using a sequence of image processing operators; (ii) estimate T1, T2 and PD by fitting the preprocessed data to an imaging equation; (iii) perform a fuzzy c-means clustering on the same preprocessed data to obtain a spatial map representing the membership value of the three tissue classes at each pixel location; (iv) reject estimates which are not from pure tissue or have poor fits in the parameter estimation, and classify the remaining estimates as either GM, WM or CSF; (v) compute statistics on the classified estimates to obtain a probability mass function and a Gaussian joint pdf of the tissue parameters for each tissue class. Some preliminary results are shown comparing computed pdf's of young, elderly and Alzheimer's subjects. Two brief examples applying the joint pdf's to pulse sequence optimization and generation of computational phantoms are also provided.
1958666
A 2D moving grid geometric deformable model
Geometric deformable models based on the level set method have become very popular. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. However, this strategy requires a very complex data structure, yields large numbers of contour points, and is inconsistent with our previously presented topology-preserving geometric deformable model (TGDM). In this paper, we incorporate an alternative adaptive grid technique called the moving grid method into the geometric deformable model framework. We find that it is simpler to implement than grid refinement, requiring no large, complex, hierarchical data structures. It also limits the number of contour vertices in the final contour and supports the incorporation of the topology-preserving constraint of TGDM. After presenting the algorithm, we demonstrate its performance using both simulated and real images.
3207638
A topology preserving deformable model using level sets
Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models, the ability to automatically handle topology changes, turns out to be a liability in applications where the objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology. This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.
980797
Gradient vector flow: a new external force for snakes
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to concave boundaries, however, have limited their utility. This paper develops a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF) is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The resultant field has a large capture range and forces active contours into concave regions. Examples on simulated images and one real image are presented.
15546541
Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images
Reconstruction of the human cerebral cortex from MR images is a fundamental step in human brain mapping and in applications such as surgical path planning. In a previous paper, we described a method for obtaining a surface representation of the central layer of the human cerebral cortex using fuzzy segmentation and a deformable surface model. This method, however, suffers from several problems. In this paper, we significantly improve upon the previous method by using a fuzzy segmentation algorithm robust to intensity inhomogeneities, and using a deformable surface model specifically designed for capturing convoluted sulci or gyri. We demonstrate the improvement over the previous method both qualitatively and quantitatively, and show the result of its application to six subjects. We also experimentally validate the convergence of the deformable surface initialization algorithm.
6175091
Snakes, shapes, and gradient vector flow
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
1880898
Energy-aware modeling and scheduling of real-time tasks for dynamic voltage scaling
Dynamic voltage scaling (DVS) is a promising technique for battery-powered systems to conserve energy consumption. Most existing DVS algorithms assume information about task periodicity or a priori knowledge about the task set to be scheduled. This paper presents an analytical model of general tasks for DVS assuming job timing information is known only after a task release. It models the voltage scaling process as a transfer function-based filter system, which facilitates the design of two efficient scaling algorithms. The first is a time-invariant scaling policy based on a voltage scaling function independent of input jobs over time. It is proved to be a generalization of several existing DVS algorithms. A more energy efficient policy is a time-variant scaling algorithm. The algorithm turns out to be a water-filling process of information theory with a low time complexity. It can not only be applied to scheduling based on worst case execution times, but also to online slack distribution when jobs complete earlier. We further establish two relationships between computation capacity and deadline misses. The relationships make it possible to the provisioning of statistical real-time guarantees
29283235
Request redirection and data layout for network traffic balancing in cluster-based video-on-demand s
Cluster architecture is a cost-effective approach to building up scalable servers. For I/O-intensive videoon-demand (VoD) applications, network bandwidth is usually the primary bottleneck. In this paper, we propose a request redirection strategy to balance the network traffic in cluster-based VoD servers. The redirection strategy utilizes the servers' internal backbone bandwidth to equalize their external network traffic. Performance of redirection is related with data layouts based on replication. We implemented two replication methods and two placement methods for videos with different popularities. Furthermore, we evaluated redirection strategy with different data layouts. The simulation results verify the effectiveness of the redirection strategy and proposed data layout methods.
26427901
Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
We consider the problem of execution time prediction for non-deterministic multi-phase bulk synchronous computations in multiprocessors. We characterize the computations in two stochastic workload evolution models: additive and multiplicative. The additive model reflects the commutations in which the workload changes between phases are independent of processes' present workload. The multiplicative model becomes relevant when the workload change in a process is proportional to its load base. We take advantage of their salient features and show that conventional approaches based on central limit theorem in statistics are viable to predict the execution time for long run computations. By an elegant coordination of results from order statistics and convergence rates in the central limit theorem, we derive tighter bounds on execution time of short run computations, under some mild assumptions on their workload change distributions. Accuracy of the predictions is analyzed rigorously and verified by simulations.
14333559
Time-stamping algorithms for parallelization of loops at run-time
In this paper we present two new run-time algorithms for the parallelization of loops that have indirect access patterns. The algorithms can handle any type of loop-carried dependencies. They follow the INSPECTOR/EXECUTOR scheme and improve upon previous algorithms with the same generality by allowing concurrent reads of the same location and by increasing the overlap of dependent iterations. The algorithms are based on time-stamping rules and implemented using multithreading tools. The experimental results on an SMP server with four processors show that our schemes are efficient and outperform their competitors consistently in all test cases. The difference between the two proposed algorithms is that one allows partially concurrent reads without causing extra overhead in its inspector while the other allows fully concurrent reads at a slight overhead in the dependence analysis. The algorithm allowing fully concurrent reads obtains up to an 80% improvement over its competitor.
17355538
Distributed shared arrays: A distributed virtual machine with mobility support for reconfiguration
Distributed Shared Arrays (DSA) is a distributed virtual machine that supports Java-compliant multithreaded programming with mobility support for system reconfiguration in distributed environments. The DSA programming model allows programmers to explicitly control data distribution so as to take advantage of the deep memory hierarchy, while relieving them from error-prone orchestration of communication and synchronization at run-time. The DSA system is developed as an integral component of mobility support middleware for Grid computing so that DSA-based virtual machines can be reconfigured to adapt to the varying resource supplies or demand over the course of a computation. The DSA runtime system also features a directory-based cache coherence protocol in support of replication of user-defined sharing granularity and a communication proxy mechanism for reducing network contention. System reconfiguration is achieved by a DSA service migration mechanism, which moves the DSA service and residing computational agents between physical servers for load balancing and fault resilience. We demonstrate the programmability of the model in a number of parallel applications and evaluate its performance by application benchmark programs, in particular, the impact of the coherence granularity and service migration overhead.