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检索条件"主题词=Expectation maximization algorithms"

共 128 条记录，以下是91-100 订阅

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LEARNING THE IDENTITY WITH BASIC MORPHOLOGICAL OPERATORS (POTATO PEELER ALGORITHM) 5

LEARNING THE IDENTITY WITH BASIC MORPHOLOGICAL OPERATORS (PO...

引用

Conference on Image Algebra and Morphological Image Processing V

作者： MATTIOLI, J SCHMITT, M THOMSON CSF LCRF-91404 ORSAYFRANCE

Let f denote an image and F a morphological operator depending on a set of parameters {mj}. The purpose of this paper is to find a set of parameters which satisfies the equation F(f,m) equals f where F is one of the t... 详细信息

ISBN: (纸本)081941624X

Let f denote an image and F a morphological operator depending on a set of parameters {m_j}. The purpose of this paper is to find a set of parameters which satisfies the equation F(f,m) equals f where F is one of the two basic morphological operator: erosion or dilation. It is obvious that there exists a trivial solution m of f equals F(f,m), which is given by m(x) equals 0 if x equals 0 and m(x) equals -(infinity) otherwise. Our approach is to consider this problem as an optimization problem: `find the best set of parameters which minimizes the error between the desired image f and the output filtered image F(f,m)'. Among various possible approaches, we have chosen a specific one introduced by Ph. Salembier for adaptative structuring elements. In our problem, we prove that Salembier algorithms always converge toward a solution distinct from m.

关键词： Chemical elements Image filtering Americium expectation maximization algorithms Fourier transforms Lithium Neodymium On-screen displays Plutonium Stochastic processes

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Multi-Atlas-based Tissue Identification in the Lower Leg using pQCT

Multi-Atlas-based Tissue Identification in the Lower Leg usi...

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Medical Imaging Conference - Image Processing

作者： Makrogiannis, Sokratis Okorie, Azubuike Biswas, Taposh Ferrucci, Luigi Delaware State Univ Div Phys Engn Math & Comp Sci Dover DE 19901 USA NIA NIH Baltimore MD 21224 USA

ISBN: (纸本)9781510633940

Accurate and reproducible tissue identification techniques are essential for understanding structural and functional changes that either occur naturally with aging, or because of chronic disease, or in response to intervention therapies. These image analysis techniques are frequently utilized for characterization of changes in bone architecture to assess fracture risk, and for the assessment of loss of muscle mass and strength defined as sarcopenia. Peripheral quantitative computed tomography (pQCT) is widely employed for tissue identification and analysis. Advantages of pQCT scanners are compactness, portability, and low radiation dose. However, these characteristics imply limitations in spatial resolution and SNR. Therefore, there is still a need for segmentation methods that address image quality limitations and artifacts such as patient motion. In this paper, we introduce multi-atlas segmentation (MAS) techniques to identify soft and hard tissues in pQCT scans of the proximal tibia (similar to 66% of tibial length) and to address the above factors that limit delineation accuracy. To calculate the deformation fields, we employed multi-grid free-form deformation (FFD) models with B-splines and a symmetric extension of the log-domain diffeomorphic demons (SDD). We then applied majority voting and Simultaneous Truth And Performance Level Estimation (STAPLE) for label fusion. We compared the results of our MAS methodology for each deformable registration model and each label fusion method, using Dice similarity coefficient scores (DSC). The results show that our technique utilizing SDD with STAPLE produces very good accuracy (DSC mean of 0.868) over all tissues, even for scans with considerable quality degradations caused by motion artifacts.

关键词： Tissues Image segmentation Bone expectation maximization algorithms Image quality Motion models Clinical trials Signal to noise ratio Fusion energy Medical imaging

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Improving whole-brain segmentations through incorporating regional image intensity statistics

Improving whole-brain segmentations through incorporating re...

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Conference on Medical Imaging - Image Processing

作者： Ledig, Christian Heckemann, Rolf A. Hammers, Alexander Rueckert, Daniel Univ London Imperial Coll Sci Technol & Med Dept Comp Biomed Image Anal Grp London SW7 2AZ England

ISBN: (纸本)9780819494436

Multi-atlas segmentation methods are among the most accurate approaches for the automatic labeling of magnetic resonance (MR) brain images. The individual segmentations obtained through multi-atlas propagation can be combined using an unweighted or locally weighted fusion strategy. Label overlaps can be further improved by refining the label sets based on the image intensities using the expectation-Maximisation (EM) algorithm. A drawback of these approaches is that they do not consider knowledge about the statistical intensity characteristics of a certain anatomical structure, especially its intensity variance. In this work we employ learned characteristics of the intensity distribution in various brain regions to improve on multi-atlas segmentations. Based on the intensity profile within labels in a training set, we estimate a normalized variance error for each structure. The boundaries of a segmented region are then adjusted until its intensity characteristics are corrected for this variance error observed in the training sample. Specifically, we start with a high-probability "core" segmentation of a structure, and maximise the similarity with the expected intensity variance by enlarging it. We applied the method to 35 datasets of the OASIS database for which manual segmentations into 138 regions are available. We assess the resulting segmentations by comparison with this gold-standard, using overlap metrics. Intensity-based statistical correction improved similarity indices (SI) compared with EM-refined multi-atlas propagation from 75.6% to 76.2% on average. We apply our novel correction approach to segmentations obtained through either a locally weighted fusion strategy or an EM-based method and show significantly increased similarity indices.

关键词： Image segmentation Brain expectation maximization algorithms Image fusion Error analysis Neuroimaging Americium Magnetic resonance imaging Image processing algorithms and systems Magnetism

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Sparse Conditional Mixture Model: Late Fusion with Missing Scores for Multimedia Event Detection

Sparse Conditional Mixture Model: Late Fusion with Missing S...

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Conference on Multimedia Content and Mobile Devices

作者： Nallapati, Ramesh Yeh, Eric Myers, Gregory SRI Int Menlo Pk CA 94025 USA

ISBN: (纸本)9780819494405

The problem of event detection in multimedia clips is typically handled by modeling each of the component modalities independently, then combining their detection scores in a late fusion approach. One of the problems of a late fusion model in the multimedia setting is that the detection scores may be missing from one or more components for a given clip;e.g., when there is no speech in the clip;or when there is no overlay text. Standard fusion techniques typically address this problem by assuming a default backoff score for a component when its detection score is missing for a clip. This may potentially bias the fusion model, especially if there are many missing detections from a given component. In this work, we present the Sparse Conditional Mixture Model (SCMM) which models only the observed detection scores for each example, thereby avoiding making any assumptions about the distributions of the scores that are made by backoff models. Our experiments in multi-media event detection using the TRECVID-2011 corpus demonstrates that SCMM achieves statistically significant performance gains over standard late fusion techniques. The SCMM model is very general and is applicable to fusion problems with missing data in any domain.

关键词： Multimedia Data modeling Binary data Data fusion Systems modeling Motion models Video Visual process modeling expectation maximization algorithms Performance modeling

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POISSON MAP SUPERRESOLUTION ESTIMATOR WITH SMOOTHNESS CONSTRAINT 2

POISSON MAP SUPERRESOLUTION ESTIMATOR WITH SMOOTHNESS CONSTR...

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CONF ON NEURAL AND STOCHASTIC METHODS IN IMAGE AND SIGNAL PROCESSING 2

作者： SEMENTILLI, PJ NADAR, MS HUNT, BR Univ. of Arizona (United States)

ISBN: (纸本)0819412813

A well known problem associated with super-resolution of imagery is the introduction of oscillatory artifacts into the super-resolved object estimate. In this paper, we derive a Maximum A-Posteriori (MAP) object estimator subject to a constraint on the norm of the first differences of adjacent object pixels. The posterior density is derived from a Poisson observation model and a Poisson prior for the object. It is shown that this optimization problem is identical to a MAP estimator with a Markov Random Field (MRF) prior using zero- and first-order neighborhood cliques. While most MRF priors used for image restoration neglect the singleton clique, our model incorporates this additional a-priori object knowledge into the estimator. An iterative solution to the optimization problem is developed using the Picard iteration technique. Results are presented which demonstrate substantial artifact reduction while achieving the bandwidth extension necessary to accomplish super-resolution. Test cases include extended objects and natural scenes.

关键词： expectation maximization algorithms Super resolution Algorithm development Image restoration Magnetorheological finishing algorithms Convolution Image processing Optimization (mathematics) Imaging systems

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Wavelet Domain Denoising by Using the Universal Hidden Markov Tree Model

Wavelet Domain Denoising by Using the Universal Hidden Marko...

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Conference on Applications of Digital Image Processing XXXI

作者： Li, Feng Fraser, Donald Jia, Xiuping Lambert, Andrew Univ New S Wales Sch Informat Technol & Elect Engn ADFA Canberra ACT Australia

ISBN: (纸本)9780819472939

In this paper, a new image denoising method which is based on the uHMT(universal Hidden Markov Tree) model in the wavelet domain is proposed. The MAP (Maximum a Posteriori) estimate is adopted to deal with the ill-conditioned problem (such as image denoising) in the wavelet domain. The uHMT model in the wavelet domain is applied to construct a prior model for the MAP estimate. By using the optimization method Conjugate Gradient, the closest approximation to the true result is achieved. The results show that images restored by our method are much better and sharper than other methods not only visually but also quantitatively.

关键词： Wavelets Image denoising Denoising expectation maximization algorithms Wavelet transforms Image restoration Discrete wavelet transforms Remote sensing Silicon Visualization

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MAP IMAGE-RECONSTRUCTION USING INTENSITY AND LINE PROCESSES FOR EMISSION TOMOGRAPHY DATA 2

引用

CONF ON IMAGE PROCESSING algorithms AND TECHNIQUES

作者： YAN, XH LEAHY, R Univ. of Southern California (United States)

ISBN: (纸本)0819405515

A Bayesian approach to image reconstruction from emission tomography image is presented in which the image is modeled using a joint Gibbs distribution of emission intensities and line processes. The line process represents the presence or absence of discontinuities between each neighboring pair of pixels. It is introduced to avoid the smoothing across discontinuities, which commonly occurs in Bayesian image estimation when a line process is not included. Two algorithms for MAP estimation over both intensity and line processes are presented. Both methods employ the generalized EM (GEM) algorithm to avoid direct optimization over the posterior distribution which does not share the Markovian property of the prior. The M-step of the GEM algorithm of the MAP estimation problem requires optimization over a function which has the appealing property that the neighborhood is identical to that of the prior. During the M-step both the intensity and line processes are updated. This is achieved in two stages. In the M1-step the intensities are updated, while holding the line process constant, using a gradient descent method. In the M2-step the line process is updated, with the intensity process held constant. Two alternative M2-steps are described in the paper. The use of a line process in the image model also provides a natural framework for the incorporation of a priori information from other modalities. In this case, boundaries may be found from MR or CT images and used as known line processes in the image estimation procedure.

关键词： Image processing Reconstruction algorithms expectation maximization algorithms Positron emission tomography Image restoration Algorithm development Image segmentation Magnetic resonance imaging Tomography Sensors

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Image reconstruction algorithms for a novel PET system with a half-ring insert.

Image reconstruction algorithms for a novel PET system with ...

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Conference on Computational Imaging IV

作者： Pal, Debashish Tai, Yuan-Chuan Janecek, Martin Wu, Heyu O'Sullivan, Joseph A. Washington Univ Biomed Engn St Louis MO 63130 USA Washington Univ Mallinckrodt Insititute Radiol St Louis MO 63130 USA Washington Univ Elect & Syst Engn St Louis MO 63130 USA

ISBN: (纸本)0819461059

Breast cancer continues to be the most common malignancy of women in the United States. Nuclear imaging techniques such as positron emission tomography (PET) have been widely used for the staging of cancer. The primary limitations of PET for breast cancer diagnosis are the lack of a highly specific radiotracer and the limited resolution of imaging systems. The sensitivity for detecting small lesions is very low. Many groups are developing positron emission mammography (PEM) systems dedicated for breast imaging using high resolution detectors. Although image resolution is significantly improved compared to whole-body PET systems, the clinical value of a PEM system is yet to be proven,^3.4 Most PET systems have limitations in imaging tissues near the chest walls and lymph nodes. The proposed system addresses the sampling requirements specific to breast imaging and achieves high resolution in PET images of breast and thorax.

关键词： Sensors Image resolution Positron emission tomography expectation maximization algorithms Scanners Imaging systems Reconstruction algorithms Monte Carlo methods Breast Image restoration

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Statistical Shape and Appearance Models without One-to-One Correspondences

Statistical Shape and Appearance Models without One-to-One C...

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Conference on Medical Imaging - Image Processing

作者： Ehrhardt, Jan Krueger, Julia Handels, Heinz Med Univ Lubeck Inst Med Informat Lubeck Germany

ISBN: (纸本)9780819498274

One-to-one correspondences are fundamental for the creation of classical statistical shape and appearance models. At the same time, the identification of these correspondences is the weak point of such model-based methods. Hufnagel et al.(1) proposed an alternative method using correspondence probabilities instead of exact one-to-one correspondences for a statistical shape model. In this work, we extended the approach by incorporating appearance information into the model. For this purpose, we introduce a point-based representation of image data combining position and appearance information. Then, we pursue the concept of probabilistic correspondences and use a maximum a-posteriori (MAP) approach to derive a statistical shape and appearance model. The model generation as well as the model fitting can be expressed as a single global optimization criterion with respect to model parameters. In a first evaluation, we show the feasibility of the proposed approach and evaluate the model generation and model-based segmentation using 2D lung CT slices.

关键词： Image segmentation Data modeling Statistical modeling Process modeling Lung Visual process modeling expectation maximization algorithms Image registration Shape analysis Optimization (mathematics)

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MULTISCALE STOCHASTIC APPROACH TO OBJECT DETECTION

MULTISCALE STOCHASTIC APPROACH TO OBJECT DETECTION

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Meeting on Visual Communications and Image Communication 93

作者： TRETTER, D BOUMAN, CA PURDUE UNIV W LAFAYETTEIN 47907

ISBN: (纸本)0819413690

We present a method for object detection based on a novel multiscale stochastic model together with Bayesian estimation techniques. This approach results in a fast, general algorithm which may be easily trained for specific objects. The object model is based on a stochastic tree structure in which each node is an important subassembly of the three dimensional object. Each node or subassembly is modeled using a Gaussian pyramid decomposition. The objective of the algorithm is then to estimate the unknown position of each subassembly, and to determine on the presence of the object. We use a fast multiscale search technique to compute the sequential MAP (SMAP) estimate of the unknown position, scale factor, and 2-D rotation for each subassembly. The search is carried out in a manner similar to a sequential likelihood ratio test, where the process advances in scale rather than time. We use a similar search to estimate the model parameters for a given object from a set of training images.

关键词： Stochastic processes Image resolution 3D modeling Data modeling expectation maximization algorithms Detection and tracking algorithms Image processing Error analysis Polonium Object recognition

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