Automated Segmentation of Sulcal Regions
Automatic segmentation and identification of cortical sulci play an important role in the study of brain structure and function. In this work, a method is presented for the automatic segmentation of sulcal regions of cortex. Unlike previous methods that extract the sulcal spaces within the cortex, the proposed method extracts actual regions of the cortical surface that surround sulci. Sulcal regions are segmented from the medial surface as well as the lateral and inferior surfaces. The method first generates a depth map on the surface, computed by measuring the distance between the cortex and an outer “shrink-wrap” surface. Sulcal regions are then extracted using a hierarchical algorithm that alternates between thresholding and region growing operations. To visualize the buried regions of the segmented cortical surface, an efficient technique for mapping the surface to a sphere is proposed. Preliminary results are presented on the geometric analysis of sulcal regions for automated identification.

title: A 2D moving grid geometric deformable model
abstract: 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.

title: Gradient vector flow: a new external force for snakes
abstract: 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.

title: Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images
abstract: 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.

title: A topology preserving deformable model using level sets
abstract: 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.

title: Reconstruction of the human cerebral cortex from magnetic resonance images
abstract: Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. Here the authors address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented.

title: Optimal Parameters for Load Balancing with the Diffusion Method in Mesh Networks
abstract: The diffusion method is a simple distributed load balancing method for distributed memory multiprocessors. It operates in a relaxation fashion for point-to-point networks. Its convergence to the balanced state relies on the value of a parameter—the diffusion parameter. An optimal diffusion parameter would lead to the fastest convergence of the method. Previous results on optimal parameters have existed for the k-ary n-cube and the torus. In this paper, we derive optimal diffusion parameters for mesh networks.

title: Elastic Routing Table with Provable Performance for Congestion Control in DHT Networks
abstract: Distributed hash table (DHT) networks based on consistent hashing functions have an inherent load balancing problem. The problem becomes more severe due to the heterogeneity of network nodes and the non-uniform and timevarying file popularity. Existing DHT load balancing algorithms are mainly focused on the issues caused by node heterogeneity. To deal with skewed lookups, this paper presents an elastic routing table (ERT) mechanism for query load balancing, based on the observation that high degree nodes tend to experience more traffic load. The mechanism allows each node to have a routing table of variable size corresponding to its capacity. The indegree and outdegree of the routing table can also be adjusted dynamically in response to the change of file popularity and network churn. Theoretical analysis proves the routing table degree is bounded. The ERT mechanism facilitates locality-aware randomized query forwarding to further improve lookup efficiency. By relating query forwarding to a supermarket customer service model, we prove a 2-way randomized query forwarding policy leads to an exponential improvement in query processing time over random walking. Simulation results demonstrate the effectiveness of the ERT mechanism and its related query forwarding policy for congestion and query load balancing. In comparison with the existing "virtual-server"-based load balancing algorithm and other routing table control approaches, the ERT-based congestion control protocol yields significant improvements in query lookup efficiency.

title: Towards semantics-based prefetching to reduce Web access latency
abstract: Prefetching is an important technique for tolerating Web access latency. Existing prefetching algorithms are mostly based on URL graphs. While they have been demonstrated to be effective in prefetching of documents that are often accessed, few of them can prefetch documents whose URLs have never been accessed. We propose a semantics-based prefetching technique to overcome the limitation. It predicts future requests based on semantic preferences of previously retrieved documents. We apply this technique to news reading activities and prototyped a client-side prefetching system, NewsAgent. The system extracts document semantics by identifying keywords in their URL anchor texts and relies on neural networks over the keyword set to predict future requests. We cross-examine the system in daily browsing of ABC News, CNN, and MSNBC News sites for three months and demonstrate the effectiveness of the technique.

title: Time-stamping algorithms for parallelization of loops at run-time
abstract: 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.

title: Stochastic modeling and analysis of hybrid mobility in reconfigurable distributed virtual machines
abstract: Virtualization provides a vehicle to manage the available resources and enhance their utilization in network computing. System dynamics requires virtual machines be distributed and reconfigurable. To construct reconfigurable distributed virtual machines, service migration moves the runtime services among physical servers when necessary. By incorporating the mobile agent technology, distributed virtual machines can improve their resource utilization and service availability significantly. This paper focuses on finding the optimal migration policies for service and agent migrations for high throughput in reconfigurable distributed virtual machines. We analyze three issues of this decision problem: migration candidate determination, migration timing and destination server selection. The service migration timing and destination server selection are formulated by two optimization models. We derive the optimal migration policy for distributed and heterogenous systems based on stochastic optimization theories. Renewal processes are applied to model the dynamics of migration. We solve the agent migration problem by dynamic programming and extend the optimal service migration decision by considering the interplay of the hybrid mobility. We verify the accuracy of our migration decision policy in simulations.