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
14441710
Applying statistical methodology to optimize and simplify software metric models with missing data
During the construction of a software metric model, the decision on whether a particular predictor metric should be included is most likely based on an intuitive or experience based assumption that the predictor metric has an impact on the target metric with a statistical significance. However, a model constructed based on such an assumption may contain redundant predictor metric(s) and/or unnecessary predictor metric complexity. This is because the assumption made before the model construction is not verified after the model is constructed. To resolve the first problem (i.e., possible redundant predictor metric(s)), we propose a statistical hypothesis testing methodology to verify "retrospectively" the statistical significance of the impact of each predictor metric on the target metric. If the variation of a predictor metric does not correlate enough with the variation of the target metric, the predictor metric should be deleted from the model. For the second problem (i.e., unnecessary predictor metric complexity), we use "goodness-of-fit" to determine whether certain categories of a categorical predictor metric should be combined together. In addition, missing data often appear in the data sample used for constructing the model. We use a modified k-nearest neighbors (k-NN) imputation method to deal with this problem. A study using data from the "Repository Data Disk - Release 6" is reported. The results indicate that our methodology can be useful in trimming redundant predictor metrics and identifying unnecessary categories initially assumed for a categorical predictor metric in the model.
26732405
BaLinda Lisp: Design and Implementation
BaLinda Lisp is a parallel Lisp dialect employing a shared memory model of parallel processing together with a subset of the Linda tuple space operators. Also included in the design is speculative processing. The aim is to have shared memory multiprocessing with the tuple space acting as the main data structure through which synchronization is done. The result is a flexible programming language. In this paper, we shall describe the design philosophy of BaLinda Lisp, discuss its implementation on three multiprocessing platforms, give examples of programs written in BaLinda Lisp and discuss future work on the language.
42032352
Static and dynamic distance metrics for feature-based code analysis
In this paper we present metrics 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 metrics, we report a case study on a Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE). The results of our study suggest that the distance metrics 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.
6423442
Rule-based anomaly pattern detection for detecting disease outbreaks
This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We propose using a rule-based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.
14591314
Optimizing and simplifying software metric models constructed using maximum likelihood methods
A software metric model can be used to predict a target metric (e.g., the development work effort) for a future release of a software system based on the project's predictor metrics (e.g., the project team size). However, missing or incomplete data often appear in the data samples used to construct the model. So far, the least biased and thus the most recommended software metric models for dealing with the missing/incomplete data are those constructed by using the maximum likelihood methods. It is true that the inclusion of a particular predictor metric in the model construction is initially based on an intuitive or experience-based assumption that the predictor metric impacts significantly the target metric. Nevertheless, this assumption has to be verified. Previous research on metric models constructed by using the maximum likelihood methods simply took this verification for granted. This can result in probable inclusion of superfluous predictor metric(s) and/or unnecessary predictor metric complexity. In this paper, we propose a methodology to optimize and simplify such models based on the results of appropriate hypothesis tests. An experiment is also reported to demonstrate the use of our methodology in trimming redundant predictor metric(s) and/or unnecessary predictor metric complexity.