32074986
Statistical Neighbor Distance Influence in Active Contours
In this paper, we propose a new model for segmentation of images containing multiple objects. In order to take advantage of the constraining information provided by neighboring objects, we incorporate information about the relative position and shape of neighbors into the segmentation process by defining a new "distance" term into the energy functional. We introduce a representation for relative neighbor distances, and define a probability distribution over the variances of the relative neighbor distances of a set of training images. By minimizing the energy functional, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the relative distance information and the image grey level information. Several objects in an image can be automatically detected simultaneously.
123222454
Joint prior models of neighboring objects for 3D image segmentation
This paper presents a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of multiple objects, along with information derived from the input image, is employed. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. In contrast to the work presented earlier, we define a maximum a posteriori (MAP) estimation model using the joint prior information of the multiple objects to realize image segmentation, which allows multiple objects with clearer boundaries to be reference objects to provide constraints in the segmentation of difficult objects. To achieve this, multiple signed distance functions are employed as representations of the objects in the image. We introduce a representation for the joint density function of the neighboring objects, and define joint probability distribution over the variations of objects contained in a set of training images. By estimating the MAP shapes of the objects, we formulate the joint shape prior models in terms of level set functions. We found the algorithm to be robust to noise and able to handle multidimensional data. Furthermore, it avoids the need for point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
11796157
Functional Brain Image Analysis Using Joint Function-Structure Priors
We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.
2345378
Segmentation of 3D deformable objects with level set based prior models
We propose a level set based deformable model for the segmentation of multiple objects from 3D medical images using shape prior constraints. As an extension to the level set distribution model of object shape presented in previous papers, this paper evaluates the performance of the level set representation of the object shape by comparing it with the point distribution model(PDM) using the Chi-square test. We define a maximum a posteriori (MAP) estimation model using level set based prior information to realize the segmentation of the multiple objects. To achieve this , only one level set function is employed as the representation of the multiple objects of interest within the image. We then define the probability distribution over the variations of objects contained in a set of training images. We found the algorithm to be computationally efficient, robust to noise, able to handle multidimensional data, and avoids the need for explicit point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
24960929
3D Image Segmentation of Deformable Objects with Shape-Appearance Joint Prior Models
We propose a novel method for 3D image segmentation, where a Bayesian formulation, based on joint prior knowledge of the shape and the image gray levels, along with information derived from the input image, is employed. Our method is motivated by the observation that the shape of the object and the gray level variation in an image have consistent relations that provide configurations and context that aid in segmentation. We define a Maximum A Posteriori(MAP) estimation model using the joint prior information of the shape and image gray levels to realize image segmentation. We introduce a representation for the joint density function of the object and the image gray level values, and define joint probability distribution over the variations of object shape and the gray levels contained in a set of training images. By estimating the MAP shape of the object, we formulate the shape-appearance model in terms of level set function as opposed to landmark points of the shape. We found the algorithm to be robust to noise, able to handle multidimensional data, and avoiding the need for point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
207143686
Estimating The Attribute Weights Through Evidential Reasoning And Mathematical Programming
An approach for estimating attribute weight is proposed, which is addressed to the problem inverse to the aggregation assessment based on a hierarchy of attributes. Evidential reasoning framework is employed since the assessment on some basic attributes may be uncertain and incomplete. As a by-product, a simple formula for evidence combination for our special case is obtained. The weights are estimated based on the preference given by the decision-maker, and represented as intervals. The more abundant preference the decision-maker shows, the clearer the relation among the basic attributes.
14440555
AUTOBIB: automatic extraction of bibliographic information on the Web
The Web has greatly facilitated access to information. However, information presented in HTML is mainly intended to be browsed by humans, and the problem of automatically extracting such information remains an important and challenging task. In this work, we focus on building a system called AUTOBIB to automate extraction of bibliographic information on the Web. We use a combination of bootstrapping, statistical, and heuristic methods to achieve a high degree of automation. To set up extraction from a new site, we only need to provide a few lines of code specifying how to download pages containing bibliographic information. We do not need to be concerned with each site's presentation format, and the system can cope with changes in the presentation format without human intervention. AUTOBIB bootstraps itself with a small seed database of structured bibliographic records. For each bibliographic Web site, we identify segments within its pages that represent bibliographic records, using state-of-the-art record-boundary discovery techniques. Next, we find matches for some of these "raw records" in the seed database using a set of heuristics. These matches serve as a training set for a parser based on the hidden Markov model (HMM), which is then used to parse the rest of the raw records into structured records. We have found an effective HMM structure with special states that correspond to delimiters and HTML tags in raw records. Experiments demonstrate that for our application, this HMM structure achieves high success rates without the complexity of previously proposed structures.
1498767
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.
7776820
Scalable continuous query processing by tracking hotspots
This paper considers the problem of scalably processing a large number of continuous queries. We propose a flexible framework with novel data structures and algorithms for group-processing and indexing continuous queries by exploiting potential overlaps in query predicates. Our approach partitions the collection of continuous queries into groups based on the clustering patterns of the query ranges, and then applies specialized processing strategies to those heavily-clustered groups (or hotspots). To maintain the partition dynamically, we present efficient algorithms that maintain a nearly optimal partition in nearly amortized logarithmic time. We show how to use the hotspots to scalably process large numbers of continuous select-join and band-join queries, which are much more challenging than simple range selection queries. Experiments demonstrate that this approach can improve the processing throughput by orders of magnitude. As another application of hotspots, we show how to use them to build a high-quality histogram for intervals in linear time.
20784712
A fault-tolerant distributed channel allocation scheme for cellular networks
In cellular networks, it is vital to allocate communication channels efficiently because the bandwidth allocated for cellular communication is limited. If channels are statically allocated, as is the case in current cellular networks, a cell may run out of channels when a large number of mobile hosts move to the cell. To overcome this problem, dynamic channel allocation approaches have been proposed. Under dynamic channel allocation, channels are allocated to cells on demand, thus increasing channel utilization. Such channel allocation approaches fall under two categories, namely, centralized and distributed. Centralized approaches are neither scalable nor reliable, while distributed approaches have the potential to be both reliable and scalable. In the distributed approaches, a mobile service station is responsible for allocating channels to mobile hosts in the same cell. In cellular networks, mobile service stations may fail. In addition, the network may encounter intermittent network congestion and/or link failures. It is desirable for a channel allocation algorithm to work well, even in the presence of network congestion, link failures, and/or mobile service station failures. In this paper, we present a distributed dynamic channel allocation scheme for cellular networks that is fault-tolerant. Our approach can tolerate the failure of mobile service stations, link failure, and network congestion, and make efficient reuse of channels. We also provide results of the performance evaluation of our algorithm.