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Conference on Medical Imaging 2010 - Computer-Aided Diagnosis

作者： Tao, Yimo Lo, Shih-Chung B. Freedman, Matthew T. Xuan, Jianhua Virginia Tech Dept Elect & Comp Engn Arlington VA 22203 USA Georgetown Univ Med Ctr Dept Radiol Washington DC 20007 USA Georgetown Univ Med Ctr Dept Oncol Washington DC 20057 USA

ISBN: (纸本)9780819480255

A learning-based approach integrating the use of pixel level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations. The algorithm involves a multi-phase pixel-level classification, using a comprehensive group of regional features, to generate a pixel level mass-conditional probability map (PM). Then, mass candidate along with background clutters are extracted from the PM by integrating the prior knowledge of shape and location of masses. A multi-scale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object level findings, including mass candidate, detected spiculations, and clutters, along with the PM are integrated by graph cuts to generate the final segmentation mask. The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlap ratio of 0.766 (+/- 0.144) and 0.642 (+/- 0.173) were obtained for segmenting the whole mass and only the margin portion, respectively. Williams index of area and contour based measurements indicated that segmentation results of the algorithm well agreed with the radiologists' delineation. Most importantly, the proposed approach is capable of including mass margin and its extension which are considered as key features for breast lesion analyses.

关键词： Image segmentation Phase modulation Image processing algorithms and systems Tissues Detection and tracking algorithms Image enhancement Ridge detection expectation maximization algorithms Binary data Breast

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Attraction-repulsion expectation-maximization algorithm for image processing and sensor field networks

Attraction-repulsion expectation-maximization algorithm for ...

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Conference on Visual Communications and Image Processing 2008

作者： Hong, Hunsop Schonfeld, Dan Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9780819469946

An attraction-repulsion expectation-maximization (AREM) algorithm for density estimation is proposed in this paper. We introduce a Gibbs distribution function for attraction and inverse Gibbs distribution for repulsion as an augmented penalty function in order to determine equilibrium between over-smoothing and over-fitting. The logarithm of the likelihood function augmented the Gibbs density mixture is solved under expectation-maximization (EM) method. We demonstrate the application of the proposed attraction-repulsion expectation-maximization algorithm to image reconstruction and sensor field estimation problem using computer simulation. We show, that the proposed algorithm improves the performance considerably.

关键词： expectation maximization algorithms Statistical analysis Sensors Reconstruction algorithms Autoregressive models Image restoration Signal to noise ratio Image sensors Image processing Image analysis

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A preliminary investigation of using prior information for potentially improving image reconstruction in few-view CT

A preliminary investigation of using prior information for p...

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Medical Imaging 2008 Conference

作者： Cho, Seungryong Sidky, Emil Y. Bian, Junguo Pelizzari, Charles A. Pan, Xiaochuan Univ Chicago Dept Radiol Chicago IL 60637 USA Univ Chicago Dept Radiat & Cellular Oncol Chicago IL 60637 USA

ISBN: (纸本)9780819470973

There exists a strong need to reconstruct computed tomographic (CT) images with practically useful quality from a small number of projections in image-guided radiation therapy: for lowering radiation dose delivered to the subject, for shortening the imaging time, and for reducing the imaging-configuration complexity. We have recently developed an iterative image reconstruction algorithm based on total-variation (TV) minimization from incomplete projection data in CT. In numerical studies with a variety of incomplete projection-data sets including truncated data, reduced scan range, and sparse sampling, the developed algorithm seems to yield reasonable reconstruction, as compared to some of the existing algorithms, such as algebraic reconstruction technique (ART) and expectation minimization (EM). The TV-based algorithm begins in general with a uniform image as an initial guess, and goes through iteration steps to minimize the image TV subject to satisfying the given incomplete projection data. In image-guided radiation therapy (IGRT), a patient usually undergoes CT scanning for treatment. planning, which can provide the reference image for image guidance. Therefore, we propose a TV-based algorithm with a priori information in few-view CT for IGRT, in an attempt to further reduce the number of projections needed for image reconstruction from what the TV-based algorithm uses when no a priori information is included. In this work, we report the initial results of a preliminary numerical study that we have conducted to demonstrate this approach.

关键词： Reconstruction algorithms Computed tomography expectation maximization algorithms Image restoration Algorithm development Radiotherapy CT reconstruction Image quality Detection and tracking algorithms Image registration

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Autonomously detecting the defective pixels in an imaging sensor array using a robust statistical technique

Autonomously detecting the defective pixels in an imaging se...

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Conference on Image Quality and System Performance V

作者： Ghosh, Siddhartha Marshall, Ian Freitas, Alex Univ Kent Comp Lab Canterbury CT2 7NF Kent England Univ Lancaster Ctr Environm Informat Lancaster LA1 4YQ England

ISBN: (纸本)9780819469809

We propose a statistical technique for autonomously detecting defective pixels in a CCD sensor array. Our data-driven analysis technique can autonomously identify a wide range of faulty and 'suspect' pixels (hypo-sensitive or hyper-sensitive pixels), without the need for any defect model or prior knowledge of the nature of pixel faults. We apply our technique to the autonomous detection of the defective pixels in regular images captured with a camera, equipped with a CCD.

关键词： expectation maximization algorithms Cameras Defect detection CCD cameras Imaging arrays Sensors Data modeling Calibration Charge-coupled devices Detector arrays

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Progress in mesh based spatio-temporal reconstruction

Progress in mesh based spatio-temporal reconstruction

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Conference on Computational Imaging VI

作者： Brankov, Jovan G. Gonzalo, Ricard Delgado Yang, Yongyi Jin, Mingwu Wernick, Miles N. IIT Dept Elect & Comp Engn Chicago IL 60616 USA

ISBN: (纸本)9780819469861

In this paper, we present a new methodology for calculation of a 2D projection operator for emission tomography using a content-adaptive mesh model (CAMM). A CAMM is an efficient image representation based on adaptive sampling and linear interpolation, wherein non-uniform image samples are placed most densely in regions having fine detail. We have studied CAMM in recent years and shown that a CAMM is an efficient too] for data representation and tomographic reconstruction. In addition, it can also provide a unified framework for tomographic reconstruction of organs (e.g., the heart) that undergo non-rigid deformation. In this work we develop a projection operator model suitable for a CAMM representation such that it accounts for several major degradation factors in data acquisition, namely object attenuation and depth-dependent blur in detector-collimator response. The projection operator is calculated using a ray-tracing algorithm. We tested the developed projection operator by using Monte Carlo simulation for single photon emission tomography (SPECT). The methodology presented here can also be extended to transmission tomography.

关键词： Signal attenuation Tomography Sensors Single photon emission computed tomography Monte Carlo methods Collimators Data modeling expectation maximization algorithms Image restoration Signal to noise ratio

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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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Finite mixture models for ultrawideband channels

Finite mixture models for ultrawideband channels

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Conference on Wireless Sensing and Processing III

作者： Choudhary, Divya Robinson, Aaron Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)9780819471710

In this paper we estimate finite mixture models (FMM) to describe the statistics of the ultrawideband (UWB) channel amplitudes. Various combinations of Rayleigh, Nakagami, Weibull, and Lognormal distributions are used to form the constituent probability density functions (PDFs) of the FMMs. The FMMs are identified using the Stochastic expectation maximization (SEM) algorithm. Akaike's Information Criterion is used to compare the quality of data fit provided by the FMMs and models containing only one distribution (non-mixtures). The results indicate that UWB channel amplitude statistics are best represented by mixtures of Rayleigh, Lognormal and Weibull PDFs.

关键词： Data modeling Statistical modeling expectation maximization algorithms Stochastic processes Scanning electron microscopy Antennas Process modeling Statistical analysis Time metrology Computer engineering

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Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes

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JOURNAL OF ELECTRONIC IMAGING 2007年第2期16卷 023013.1-023013.6页

作者： Zhang, Wei Fang, Xiangzhong Yang, Xiaokang Wu, Q. M. Jonathan Shanghai Jiao Tong Univ Inst Image Commun & Informat Proc Shanghai 200240 Peoples R China

The Gaussian mixture model (GMM) is an important metric for moving objects segmentation and is fit to deal with the gradual changes of illumination and the repetitive motions of scene elements. However, the performance of the GMM may be plagued by the complex motion of the dynamic background such as waving trees and flags fluttering. A spatiotemporal Gaussian mixture model (STGMM) is proposed to handle the complex motion of the background by considering every background pixel to be fluctuating both in intensity and in its neighboring region. A new matching rule is defined to incorporate the spatial information. Experimental results on typical scenes show that STGMM can segment the moving objects correctly in complex scenes. Quantitative evaluations demonstrate that the proposed STGMM performs better than GMM. (C) 2007 SPIE and IS&T.

关键词： Image segmentation Motion models Cameras Data modeling expectation maximization algorithms Performance modeling Control systems Computer engineering Data processing Filtering (signal processing)

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Modeling and estimation of wavelet coefficients using elliptically-contoured Multivariate laplace vectors

Modeling and estimation of wavelet coefficients using ellipt...

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Conference on Wavelets XII

作者： Selesnick, Ivan W. Polytech Univ Brooklyn NY 11201 USA

ISBN: (纸本)9780819468499

In this paper, we are interested in modeling groups of wavelet coefficients using a zero-mean, elliptically contoured multivariate Laplace probability distribution function (pdf). Specifically, we are interested in the problem of estimating a d-point Laplace vector, s. in additive white Gaussian noise (AWGN), n, from an observation, y = s + n. In the scalar case (d = 1), the MAP and MMSE estimators are already known;and in the vector case (d > 1), the MAP estimator can be obtained by an iterative successive substitution algorithm. For the special case where the contour of the Laplace pdf is spherical, the MMSE estimators for the vector case (d > 1) have been derived in our previous work;we have shown that the MMSE estimator can be expressed in terms of the generalized incomplete Gamma function. For the general elliptically-contoured case, the MMSE estimator can not be expressed as such. In this paper, we therefore investigate approximations to the MMSE estimator of a Laplace vector in AWGN.

关键词： Wavelets expectation maximization algorithms Data modeling Convolution Signal to noise ratio Spherical lenses Denoising Numerical integration Bessel functions Wavelet transforms

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A survey of PHD filter and CPHD filter implementations

A survey of PHD filter and CPHD filter implementations

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XVI

作者： Maher, Ronald Lockheed Martin MS2 Tact Syst Eagan MN USA

ISBN: (纸本)9780819466891

The probability hypothesis density (PHD) filter has attracted increasing interest since the author first introduced it in 2000. Potentially practical computational implementations of this filter have been devised, based on sequential Monte Carlo or on Gaussian mixture techniques. Research groups in at least a dozen different nations are investigating the PHD filter and its generalization, the CPHD filter, for use in various applications. Some of this work suggests that these filters may, under certain circumstances, outperform conventional multitarget filters such as MHT and JPDA. This paper summarizes these research efforts and their findings.

关键词： Filtering (signal processing) Electronic filtering Monte Carlo methods Target detection Detection and tracking algorithms Motion models Sensors Computer simulations expectation maximization algorithms Nonlinear filtering

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