21945290
Statistical cerebrovascular segmentation in three-dimensional rotational angiography based on maximum intensity projections
Segmentation of three-dimensional rotational angiography (3D-RA) can provide quantitative 3D morphological information of vasculature. The expectation maximization-(EM-) based segmentation techniques have been widely used in the medical image processing community, because of the implementation simplicity, and computational efficiency of the approach. In a brain 3D-RA, vascular regions usually occupy a very small proportion (around 1%) inside an entire image volume. This severe imbalance between the intensity distributions of vessels and background can lead to inaccurate statistical modeling in the EM-based segmentation methods, and thus adversely affect the segmentation quality for 3D-RA. In this paper we present a new method for the extraction of vasculature in 3D-RA images. The new method is fully automatic and computationally efficient. As compared with the original 3D-RA volume, there is a larger proportion (around 20%) of vessels in its corresponding maximum intensity projection (MIP) image. The proposed method exploits this property to increase the accuracy of statistical modeling with the EM algorithm. The algorithm takes an iterative approach to compiling the 3D vascular segmentation progressively with the segmentation of MIP images along the three principal axes, and use a winner-takes-all strategy to combine the results obtained along individual axes. Experimental results on 12 3D-RA clinical datasets indicate that the segmentations obtained by the new method exhibit a high degree of agreement to the ground truth segmentations and are comparable to those produced by the manual optimal global thresholding method.
292932
Bayesian image segmentation using local iso-intensity structural orientation
Image segmentation is a fundamental problem in early computer vision. In segmentation of flat shaded, nontextured objects in real-world images, objects are usually assumed to be piecewise homogeneous. This assumption, however, is not always valid with images such as medical images. As a result, any techniques based on this assumption may produce less-than-satisfactory image segmentation. In this work, we relax the piecewise homogeneous assumption. By assuming that the intensity nonuniformity is smooth in the imaged objects, a novel algorithm that exploits the coherence in the intensity profile to segment objects is proposed. The algorithm uses a novel smoothness prior to improve the quality of image segmentation. The formulation of the prior is based on the coherence of the local structural orientation in the image. The segmentation process is performed in a Bayesian framework. Local structural orientation estimation is obtained with an orientation tensor. Comparisons between the conventional Hessian matrix and the orientation tensor have been conducted. The experimental results on the synthetic images and the real-world images have indicated that our novel segmentation algorithm produces better segmentations than both the global thresholding with the maximum likelihood estimation and the algorithm with the multilevel logistic MRF model.
16853040
Local Orientation Smoothness Prior for Vascular Segmentation of Angiography
We present a new generic method for vascular segmentation of angiography. Angiography is used for the medical diagnosis of arterial diseases. To facilitate an effective and efficient review of the vascular information in the angiograms, segmentation is a first stage for other post-processing routines. The method we propose uses a novel a priori — local orientation smoothness prior — to enforce an adaptive regularization constraint for the vascular segmentation within the Bayes’ framework. It aspires to segment a variety of angiographies and is aimed at improving the quality of segmentation in low blood flow regions. Our algorithm is tested on numerical phantoms and clinical datasets. The experimental results show that our method produces better segmentations than the maximum likelihood estimation and the estimation with a multi-level logistic Markov random field model. Furthermore, the novel algorithm produces aneurysm segmentations comparable to the manual segmentations obtained from an experienced consultant radiologist.
1021507
Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning
Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly
38720222
Evaluation of the continuation bit in the Cyclic Pipeline Computer
Abstract The Cyclic Pipeline Computer (CPC) is a shared resource computer which shares its pipeline among multiple instruction streams to create distinct virtual processors. This effectively removes data dependencies which reduce the performance of highly pipelined computers. However, when executing unbalanced parallel programs, the CPC suffers from performance degradation due to synchronization overhead. In this paper we introduce a hardware extension called the continuation bit . The continuation bit controls the instruction issue and determines whether an instruction is issued from the same stream or the next stream. Simulations of various programs indicate that the continuation bit is able to balance the execution of parallel programs by exploiting instruction level parallelism.
14648575
dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data
MOTIVATION
Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed.


RESULT
Here, we report automated methods for pooling SNP array replicates to make LOH calls, visualizing SNP and LOH data along chromosomes in the context of genes and cytobands, making statistical inference to identify shared LOH regions, clustering samples based on LOH profiles and correlating the clustering results to clinical variables. Application of these methods to prostate and breast cancer datasets generates biologically important results.


AVAILABILITY
The software module dChipSNP implementing these methods is available at http://biosun1.harvard.edu/complab/dchip/snp/


SUPPLEMENTARY INFORMATION
The breast cancer data are provided by Andrea L. Richardson, Zhigang C. Wang and James D. Iglehart.
16024151
An adaptable architecture for secure delivery of converged services
We present an approach for secure delivery of multimodal services to end users over heterogeneous converged networks. Our approach leads to an easily adaptable and extensible system architecture, in which system components interact with each other through loosely coupled interfaces based on asynchronous exchange of messages. It also effectively separates service logic from service delivery, through the introduction of a secure broker between the end user devices and the enterprise level application that contains the service logic. This separation also provides additional levels of security at the application level. This paper describes how our approach provides the features mentioned above. We have also validated our approach through the implementation of a prototype system, a description of which is also provided.
1401424
An analytical approach to architecture-based software performance and reliability prediction
Conventional approaches to analyze the behavior of software applications are black box based, that is, the software application is treated as a whole and only its interactions with the outside world are modeled. The black box approaches ignore information about the internal structure of the application and the behavior of the individual parts. Hence, they are inadequate to model the behavior of a realistic software application, which is likely to be made up of several interacting parts. Architecture-based analysis, which seeks to assess the behavior of a software application taking into consideration the behavior of its parts and the interactions among the parts is thus essential. Most of the research in the area of architecture-based analysis has been devoted to developing analytical models, with very little, if any effort being devoted to how these models might be applied to real software applications. In order to apply these models to software applications, methods must be developed to extract the parameters of the analytical models from information collected during the execution of the application. In this paper, we present an experimental approach to extract the parameters of architecture-based models from code coverage measurements obtained during the execution of the application. To facilitate this, we use a coverage analysis tool called automatic test analyzer in C (ATAC), which is a part of Telcordia Software Visualization and Analysis Toolsuite (TSVAT) developed at Telcordia Technologies. We demonstrate the approach by predicting the performance and reliability of an application called Symbolic Hierarchical Automated Reliability Predictor (SHARPE), which has been widely used to solve stochastic models of reliability, performance and performability.
206572113
Measuring distance between program features
We present a metric to determine the distance between the features of a software system. Such a measurement can elucidate how features of the system being examined are close to each other. We first use an execution slice-based technique to identify a set of code (basic blocks in our case) that is used to implement each feature. Then, depending on whether the execution frequency of each block is considered during the construction of such sets of code, a static as well as a dynamic distance is computed for each pair of features. These two types of distance differ in that the former computes the distance between two features only by how these features are implemented in the system, while the latter also takes into account how each feature is executed based on a user's operational profile. In other words, the static distance quantitatively gives the closeness of two features from the system implementation point of view, whereas the dynamic distance presents such closeness from the users' execution point of view. To illustrate the use of our metric, we report a case study on a Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE). The results of our study suggest that the distance metric discussed in this paper can provide a good measurement, in a quantitative way, of how close two program features are. Such information can also serve as a good start to understanding how a modification made to one feature is likely to affect other features.