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.
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.
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.
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.
17586231
Non-rigid Image Registration Using Geometric Features and Local Salient Region Features
We present a novel feature-based non-rigid image registration algorithm using a small number of automatically extracted points and their associated local salient region features. Our automatic registration is a hybrid approach co-optimizing point-based and image-based terms. Motivated by the paradigm of the TPS-RPM algorithm [6], we develop the RHDM (Robust Hybrid Deformable Matching) algorithm by alternatively optimizing correspondences and transformations for registration. The local salient region features and the geometric features, together with the softassign and deterministic annealing techniques, are used for solving correspondences. Thin-plate splines are used for generating a smooth non-rigid spatial transformation. Our algorithm is built to be extremely robust to feature extraction errors. A new dynamic outlier rejection mechanism is described for rejecting outliers and generating accurate spatial mappings. A local refinement technique is used for correcting non-exactly matched correspondences arising from image noise and irregular deformations. In contrast with the TPS-RPM algorithm, which can handle only outliers in one point set, our algorithm is able to handle a considerable number of outliers in both point sets. The experimental results demonstrate the robustness and accuracy of our algorithm.
3145676
A Genetic Algorithm with Adaptive Mutations and Family Competition for Training Neural Networks
In this paper, we present a new evolutionary technique to train three general neural networks. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations to collaborate with each other. Different mutations act as global and local strategies respectively to balance the trade-off between solution quality and convergence speed. Our algorithm is then applied to three different task domains: Boolean functions, regular language recognition, and artificial ant problems. Experimental results indicate that the proposed algorithm is very competitive with comparable evolutionary algorithms. We also discuss the search power of our proposed approach.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
8849332
A cost-driven lithographic correction methodology based on off-the-shelf sizing tools
A minimum feature sizes continue to shrink, patterned features have become significantly smaller than the wavelength of light used in optical lithography. As a result, the requirements for dimensional variation control, especially in critical dimension (CD) 3/spl sigma/, has become more stringent. To meet these requirements, resolution enhancement techniques (RET) such as optical proximity correction (OPC) and phase shift mask (PSM) technologies are applied. These approaches result in a substantial increase in mask costs and make the cost of ownership (COO) a key parameter in the comparison of lithography technologies. No concept of function is injected into the mask flow; that is, current OPC techniques are oblivious to the design intent, and the entire layout is corrected uniformly with the same effort. We propose a minimum cost of correction (MinCorr) methodology to determine the level of correction for each layout feature such that prescribed parametric yield is attained with minimum total RET cost. We highlight potential solutions to the MinCorr problem and give a simple mapping to traditional performance optimization. We conclude with experimental results showing that substantial RET costs may be saved while maintaining a given desired level of parametric yield.
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.