Finding the Brain Cortex Using Fuzzy Segmentation, Isosurfaces, and Deformable Surface Models
A method for finding the cortical surface of the brain from magnetic resonance images using a combination of fuzzy segmentation, isosurface extraction, and a deformable surface is presented. After MR images are acquired and preprocessed to remove extracranial tissue, fuzzy membership functions for gray matter, white matter, and cerebrospinal fluid are computed. An iterative procedure using isosurfaces of filtered white matter membership functions is then used to obtain a topologically correct estimate of the cortical surface. This estimate forms the initialization of a deformable surface, which is then allowed to converge to peaks of the gray matter membership function. We demonstrate the results of each step and show the final parameterized map of the medial layer of the cortex.

title: Automated Segmentation of Sulcal Regions
abstract: 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 Topology Preserving Level Set Method for Geometric Deformable Models
abstract: Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models 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 object to be segmented has a known topology that must be preserved. We present a new class of geometric deformable models designed using a novel topology-preserving level set method, which achieves topology preservation by applying the simple point concept from digital topology. These new models maintain the other advantages of standard geometric deformable models including subpixel accuracy and production of nonintersecting curves or surfaces. Moreover, since the topology-preserving constraint is enforced efficiently through local computations, the resulting algorithm incurs only nominal computational overhead over standard geometric deformable models. Several experiments on simulated and real data are provided to demonstrate the performance of this new deformable model algorithm.

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: 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: 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: Optimal Remapping in Dynamic Bulk Synchronous Computations via a Stochastic Control Approach
abstract: A bulk synchronous computation proceeds in phases that are separated by barrier synchronization. For dynamic bulk synchronous computations that exhibit varying phase-wise computational requirements, remapping at runtime is an effective approach to ensure parallel efficiency. The paper introduces a novel remapping strategy for computations whose workload changes can be modeled as a Markov chain. The use of a Markovian model allows us to treat statistical dependence and more complex structure than the usual independent identically distributed random variable assumptions. Our models are quite general and we do not need to impose conditions on the dynamics of the underlying process other than the transition probability matrix. It is shown that optimal remapping can be formulated as a binary decision process: remap or not at a given synchronizing instant. The optimal strategy is then developed for long lasting computations by employing optimal stopping rules in a stochastic control framework. The existence of optimal controls is established. Necessary and sufficient conditions for the optimality are obtained. Furthermore, a policy iteration algorithm is devised to reduce computational complexity and enhance fast convergence to the desired optimal control.

title: Relaxed Implementation of Spectral Methods for Graph Partitioning
abstract: This paper presents a fast implementation of the recursive spectral bisection method for p-way partitioning. It is known that recursive bisections for p-way partitioning using optimal strategies at each step may not lead to a good overall solution. The relaxed implementation accelerates the partitioning process by relaxing the accuracy requirement of spectral bisection (SB) method. Considering the solution quality of a SB method on a graph is primarily determined by the accuracy of its Fiedler vector, we propose to set a tight iteration number bound and a loose residual tolerance for Lanczos algorithms to compute the Fiedler vector. The relaxed SB was tested on eight representative meshes from different applications. Experimental results show that the relaxed SB on six meshes produces approximately equivalent quality solutions as the Chaco SB while gaining 10% to 35% improvements in execution time. On the other two meshes, the relaxed SB approaches the Chaco SB in quality as p increases and reduces the execution time by 40% to 70%. Coupled with the Kernighan-Lin local refinement algorithm, the relaxed SB is able to yield high quality solutions in all test cases as p goes beyond 32. Multilevel and spectral quadrisection algorithms benefit from relaxed implementations, as well.

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: A flexible and recoverable client/server database event notification system
abstract: A software architecture is presented that allows client application programs to interact with a DBMS server in a flexible and powerful way, using either direct, volatile messages, or messages sent via recoverable queues. Normal requests from clients to the server and replies from the server to clients can be transmitted using direct or recoverable messages. In addition, an application event notification mechanism is provided, whereby client applications running anywhere on the network can register for events, and when those events are raised, the clients are notified. A novel parameter passing mechanism allows a set of tuples to be included in an event notification. The event mechanism is particularly useful in an active DBMS, where events can be raised by triggers to signal running application programs.

title: sMonitor: A Non-Intrusive Client-Perceived End-to-End Performance Monitor of Secured Internet Services
abstract: End-to-end performance measurement is fundamental to building high-performance Internet services. While many Internet services often operate using HTTP over SSL/TLS, current monitors are limited to plaintext HTTP services. This paper presents sMonitor, a non-intrusive server-side end-to-end performance monitor that can monitor HTTPS services. The monitor passively collects live packet traces from a server site. It then uses a size-based analysis method on HTTP requests to infer characteristics of client accesses and measures client-perceived pageview response time in real time. We designed and implemented a prototype of sMonitor. Preliminary evaluations show measurement error of less than 5%.