Results 1  10
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12
An Experimental Comparison of MinCut/MaxFlow Algorithms for Energy Minimization in Vision
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2001
"... After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time compl ..."
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Cited by 1313 (54 self)
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After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper
What energy functions can be minimized via graph cuts?
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2004
"... In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs a graph such that the minimum cut on the graph also minimizes the energy. Yet, because these graph constructions are co ..."
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Cited by 1053 (23 self)
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In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs a graph such that the minimum cut on the graph also minimizes the energy. Yet, because these graph constructions are complex and highly specific to a particular energy function, graph cuts have seen limited application to date. In this paper, we give a characterization of the energy functions that can be minimized by graph cuts. Our results are restricted to functions of binary variables. However, our work generalizes many previous constructions and is easily applicable to vision problems that involve large numbers of labels, such as stereo, motion, image restoration, and scene reconstruction. We give a precise characterization of what energy functions can be minimized using graph cuts, among the energy functions that can be written as a sum of terms containing three or fewer binary variables. We also provide a generalpurpose construction to minimize such an energy function. Finally, we give a necessary condition for any energy function of binary variables to be minimized by graph cuts. Researchers who are considering the use of graph cuts to optimize a particular energy function can use our results to determine if this is possible and then follow our construction to create the appropriate graph. A software implementation is freely available.
Mutualinformationbased registration of medical images: a survey
 IEEE Transcations on Medical Imaging
, 2003
"... Abstract—An overview is presented of the medical image processing literature on mutualinformationbased registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a s ..."
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Cited by 298 (3 self)
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Abstract—An overview is presented of the medical image processing literature on mutualinformationbased registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutualinformationbased registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges. Index Terms—Image registration, literature survey, matching, mutual information. I.
Bayesian Parallel Imaging With EdgePreserving Priors
 MAGNETIC RESONANCE IN MEDICINE
, 2007
"... Existing parallel MRI methods are limited by a fundamental tradeoff in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly o ..."
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Cited by 17 (2 self)
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Existing parallel MRI methods are limited by a fundamental tradeoff in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edgepreserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a nonconvex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.
Graph Based Algorithms for Scene Reconstruction from Two or More Views
, 2004
"... In recent years, graph cuts have emerged as a powerful optimization technique for minimizing energy functions that arise in lowlevel vision problems. Graph cuts avoid the problems of local minima inherent in other approaches (such as gradient descent). The goal of this thesis is to apply graph cuts ..."
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Cited by 11 (1 self)
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In recent years, graph cuts have emerged as a powerful optimization technique for minimizing energy functions that arise in lowlevel vision problems. Graph cuts avoid the problems of local minima inherent in other approaches (such as gradient descent). The goal of this thesis is to apply graph cuts to a classical computer vision problem — scene reconstruction from multiple views, i.e. computing the 3dimensional shape of the scene. This thesis provides a technical result which greatly facilitates the derivation of the scene reconstruction algorithm. Our result should also be useful for developing other energy minimization algorithms based on graph cuts. Previously such algorithms explicitly constructed graphs where a minimum cut also minimizes the appropriate energy. It is natural to ask for what energy functions we can construct such a graph. We answer this question for the class of functions of binary variables that can be written as a sum of terms containing three or fewer variables. We give a simple criterion for functions in this class which is necessary and sufficient, as well as a necessary condition for any function of binary variables. We also give a
Segmentation of dynamic ND data sets via graph cuts using markov models
 In Proc. Medical Image Computing and ComputerAssisted Intervention
, 2001
"... Abstract. This paper describes a new segmentation technique for multidimensional dynamic data. One example of such data is a perfusion sequence where a number of 3D MRI volumes shows the dynamics of a contrast agent inside the kidney or heart at enddiastole. We assume that the volumes are registere ..."
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Cited by 8 (0 self)
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Abstract. This paper describes a new segmentation technique for multidimensional dynamic data. One example of such data is a perfusion sequence where a number of 3D MRI volumes shows the dynamics of a contrast agent inside the kidney or heart at enddiastole. We assume that the volumes are registered. If not, we register consecutive volumes via mutual information maximization. The sequence of n registered volumes is regarded as a single volume where each voxel holds an ndimensional vector of intensities, or intensity curve. Our approach is to segment this volume directly based on voxels intensity curves using a generalization of the graph cut techniques in [7, 2]. These techniques use a spatial Markov model to describe correlations between voxels. Our contribution is in introducing a temporal Markov model to describe the desired dynamic properties of segments. Graph cuts obtain a globally optimal segmentation with the best balance between boundary and regional properties among all segmentations satisfying user placed hard constraints. Flexibility, coherent theoretical formulation, and the possibility of a globally optimal solution are attractive features of our method that gracefully handles even low quality data. We demonstrate results for 3D kidney and 2D heart perfusion sequences. 1
Exact map activity detection in fmri using a glm with an ising spatial prior,” International conference on medical image computing and computerassisted intervention (MICCAI
 In Proc. MICCAI’04
, 2004
"... Abstract. Previous work [5] has shown how Ising spatial priors [1] can be incorported into fMRI analysis in a principled manner by using Mutual Information as a statistic for protocolrelated activity. The activation image with maximum a posteriori (MAP) probability can then be computed exactly in p ..."
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Cited by 8 (0 self)
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Abstract. Previous work [5] has shown how Ising spatial priors [1] can be incorported into fMRI analysis in a principled manner by using Mutual Information as a statistic for protocolrelated activity. The activation image with maximum a posteriori (MAP) probability can then be computed exactly in polynomial time by reduction to a MinCut/MaxFlow Problem [4]. In this work, we show that an Ising prior can be applied in the same manner using a standard, linear activation model. 1
Vessel and aneurysm reconstruction using speed and flow coherence information in phase contrast magnetic resonance angiograms
, 2001
"... Phase contrast magnetic resonance angiography (PCMRA) is a noninvasive method for 3D vessel delineation, which for each voxel not only provides measurement of speed (conveyed as a speed image), but also gives a threecomponent estimate of flow direction (in the form of phase images). In this the ..."
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Cited by 1 (1 self)
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Phase contrast magnetic resonance angiography (PCMRA) is a noninvasive method for 3D vessel delineation, which for each voxel not only provides measurement of speed (conveyed as a speed image), but also gives a threecomponent estimate of flow direction (in the form of phase images). In this thesis, we present a new approach to reconstructing vessels and aneurysms from PCMRA, and demonstrate how speed and flow coherence information extracted from a PCMRA dataset can be combined for detecting and reconstructing normal vessels and aneurysms with relatively low flow rate and low signaltonoise ratio (SNR). We propose to use a MaxwellGaussian mixture density to model the background signal and combine this with a uniform distribution for modelling vascular signal to give a MaxwellGaussianuniform (MGU) mixture model of speed image intensity. The MGU model parameters are estimated by the ExpectationMaximisation (EM) algorithm. It is shown that the MaxwellGaussian mixture distribution models the background signal more accurately than a Maxwell distribution. Although the MGU model works satisfactorily in classifying the back
Information Theoretic fMRI TimeSeries Analysis
"... The Problem: Functional Magnetic Resonance Imaging (fMRI) assesses brain activity by taking a volumetric sampling of blood flow (BOLD) in the head, over time. Due to the fine temporal and spatial resolution with which fMRI can noninvasively evaluate blood flow, it is often used in experiments desig ..."
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The Problem: Functional Magnetic Resonance Imaging (fMRI) assesses brain activity by taking a volumetric sampling of blood flow (BOLD) in the head, over time. Due to the fine temporal and spatial resolution with which fMRI can noninvasively evaluate blood flow, it is often used in experiments designed to determine what regions of the brain are engaged during different activities. These includeprotocols whereby subjects perform repetitive motor or cognitive tasks, or attend to sensory stimuli, while being scanned by fMRI. We are developing Information Theoretic (IT) techniques to analyze the relationship between such protocols and fMRI time series. Motivation: By measuring blood flow, fMRI provides only an indirectindication of cognitive function. In fact, cerebral blood flow and the fMRI signal may be affected by factors extrinsic to the experimental protocol, including unrelated cognitive processes, lapses in the subject’s attention to the protocol, cardiac and respiratory fluctuations, scanner noise, and other nonGaussian, colored noise [3, 7]. Furthermore, while the BOLD response has been modeled for certain types of tasks and in certain brain regions, observed nonlinearities have not been accounted for [1]. We propose using Information Theoretic techniques to model the dependency between an fMRI timeseries and its corresponding experimental protocol. The IT framework is appealing in that it is a principled methodology requiring few assumptions about the structure of the fMRI signal, and the nature of uncertainty (i.e. Gaussianity).