Yunmei Chen

Yunmei Chen
University of Florida | UF · Department of Mathematics

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94
Publications
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Publications

Publications (94)
Chapter
In this paper, a novel network based on gated recurrent unit (GRU) is proposed for separating single-scan dual-tracer PET mixed images. Compared to conventional methods, this method can separate dual-tracer that are simultaneously injected or even labeled with the same marker, and do not require arterial blood input function. The proposed 4-layer n...
Article
Dynamic positron emission tomography (dPET) is a nuclear medical imaging technology that shows the changes in radioactivity over time. In this paper, we propose a structure and tracer kinetics-constrained reconstruction framework for dPET imaging. Given the Poisson nature of PET imaging, we integrate the sparse penalty on a dual dictionary into a P...
Article
In this study, we explore the use of low rank and sparse constraints for the noninvasive estimation of epicardial and endocardial extracellular potentials from body-surface electrocardiographic data to locate the focus of premature ventricular contractions (PVCs). The proposed strategy formulates the dynamic spatiotemporal distribution of cardiac p...
Article
Premature ventricular contraction (PVC) can cause great harm to human health. Both invasive and non-invasive techniques for detecting electrical activity of PVC or locating ectopic pacemakers are used in clinical diagnosis. Among them, the electrocardiographic imaging is a popular method for non-invasive reconstruction of cardiac electrophysiology...
Article
Purpose: Dynamic positron emission tomography (PET) is known for its ability to extract spatiotemporal information of a radio tracer in living tissue. Information of different functional regions based on an accurate reconstruction of the activity images and kinetic parametric images has been widely studied and can be useful in research and clinica...
Article
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Dynamic positron emission tomography (PET) is capable of providing both spatial and temporal information of radio tracers in vivo. In this paper, we present a novel joint estimation framework to reconstruct temporal sequences of dynamic PET images and the coefficients characterizing the system impulse response function, from which the associated pa...
Article
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In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data a...
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This paper presents a total variation (TV) regularized reconstruction algorithm for 3D positron emission tomography (PET). The proposed method first employs the Fourier rebinning algorithm (FORE), rebinning the 3D data into a stack of ordinary 2D data sets as sinogram data. Then, the resulted 2D sinogram are ready to be reconstructed by conventiona...
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Although of great clinical value, accurate and robust reconstruction and segmentation of dynamic positron emission tomography (PET) images are great challenges due to low spatial resolution and high noise. In this paper, we propose a unified framework that exploits temporal correlations and variations within image sequences based on low-rank and sp...
Article
A novel variational model for deformable multi-modal image registration is presented in this work. As an alternative to the models based on maximizing mutual information, the Renyi's statistical dependence measure of two random variables is proposed as a measure of the goodness of matching in our objective functional. The proposed model does not re...
Article
Accurate and robust reconstruction of the radioactivity concentration is of great importance in positron emission tomography (PET) imaging. Given the Poisson nature of photo-counting measurements, we present a reconstruction framework that integrates sparsity penalty on a dictionary into a maximum likelihood estimator. Patch-sparsity on a dictionar...
Conference Paper
We develop a variational model and a faster and robust numerical algorithm for simultaneous sensitivity map estimation and image reconstruction in partially parallel MR imaging with significantly under-sampled data. The proposed model uses a maximum likelihood approach to minimizing the residue of data fitting in the presence of independent Gaussia...
Article
This paper presents a variational model for simultaneous multiphase segmentation and intensity bias estimation for images corrupted by strong noise and intensity inhomogeneity. Since the pixel intensities are not reliable samples for region statistics due to the presence of noise and intensity bias, we use local information based on the joint densi...
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This paper develops a Bregman operator splitting algorithm with variable stepsize (BOSVS) for solving problems of the form $\min\{\phi(Bu) +1/2\|Au-f\|_{2}^{2}\}$ , where ϕ may be nonsmooth. The original Bregman Operator Splitting (BOS) algorithm employed a fixed stepsize, while BOSVS uses a line search to achieve better efficiency. These schemes...
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Increasingly wider availability of biomedical imaging modalities, such as X-ray computed tomography (CT), magnetic resonance imaging (MRI), ultrasonic imaging, positron emission tomography (PET), single-photon emission computed tomography (SPECT), and optical imaging, have led to significant progresses in both biomedical research and clinical pract...
Article
In this paper, we present a novel nonparametric active region model for image segmentation. This model partitions an image by maximizing the similarity between the image and a label image, which is generated by setting different constants as the intensities of partitioned subregions. The intensities of these two images can not be compared directly...
Conference Paper
In this paper, we present a multiphase segmentation model for MR images in the presence of strong intensity inhomogeneity. The problem is formalized as a constraint min-max optimization problem that consists both primal and dual variables. We use the primal dual hybrid gradient (PDHG) algorithm to alternately solve for the optimal solutions. The pr...
Conference Paper
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A new algorithm is presented for efficiently solving image reconstruction problems that arise in partially parallel magnetic resonance imaging. This algorithm minimizes an objective function of the form φ(Bu) + 1/2||FpSu - f||2, where φ is the regularization term which may be nonsmooth. In image reconstruction, the φ term corresponds to total varia...
Conference Paper
Joint image deblurring and denoising has long been an interesting problem. Traditional deconvolution methods (like the ROF model) only work for Gaussian noise. Median-based approaches are generally concerned with the removal of impulse noise, which are more likely to hamper the deblurring process. In this paper, we propose a spareland model for deb...
Article
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Genetic mapping has been used as a tool to study the genetic architecture of complex traits by localizing their underlying quantitative trait loci (QTLs). Statistical methods for genetic mapping rely on a key assumption, that is, traits obey a parametric distribution. However, in practice real data may not perfectly follow the specified distributio...
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This paper presents two fast algorithms for total variation-based image reconstruction in a magnetic resonance imaging technique known as partially parallel imaging (PPI), where the inversion matrix is large and ill-conditioned. These algorithms utilize variable splitting techniques to decouple the original problem into more easily solved subproble...
Article
In this paper, a new stochastic variational PDE model is developed, using instead of hard segmentation soft segmentation. In this way, each pixel is allowed to belong to each image pattern with some probability. Our work proposes a functional with variable exponent, which provides a more accurate model for image segmentation and denoising. The diff...
Article
A new variational region based model for a si-multaneous image segmentation and a rigid registration is proposed. The purpose of the model is to segment and register novel images simultaneously using a modified piecewise constant Mumford-Shah functional and region intensity values. The segmentation is obtained by minimizing a modified piecewise con...
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In this paper, we present a fast numerical algorithm for solving total variation and l(1) (TVL1) based image reconstruction with application in partially parallel magnetic resonance imaging. Our algorithm uses variable splitting method to reduce computational cost. Moreover, the Barzilai-Borwein step size selection method is adopted in our algorith...
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We proposed a novel framework of multiphase segmentation based on stochastic theory and phase transition theory. Our main contribution lies in the introduction of a constructed function so that its composition with phase function forms membership functions. In this way, it saves memory space and also avoids the general simplex constraint problem fo...
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Both acquisition and reconstruction speed are crucial for magnetic resonance (MR) imaging in clinical applications. In this paper, we present a fast reconstruction algorithm for SENSE in partially parallel MR imaging with arbitrary k-space trajectories. The proposed method is a combination of variable splitting, the classical penalty technique and...
Article
In this paper, we propose a novel test of independence based on the concept of correntropy. We explore correntropy from a statistical perspective and discuss its properties in the context of testing independence. We introduce the novel concept of parametric correntropy and design a test of independence based on it. We further discuss how the propos...
Article
This mini-paper presents a fast and simple algorithm to compute the projection onto the canonical simplex $\triangle^n$. Utilizing the Moreau's identity, we show that the problem is essentially a univariate minimization and the objective function is strictly convex and continuously differentiable. Moreover, it is shown that there are at most n cand...
Article
The method of enforcing sparsity during magnetic resonance imaging reconstruction has been successfully applied to partially parallel imaging (PPI) techniques to reduce noise and artifact levels and hence to achieve even higher acceleration factors. However, there are two major problems in the existing sparsity-constrained PPI techniques: speed and...
Chapter
This paper presents a novel variational model for inverse consistent deformable image registration. The proposed model deforms both source and target images simultaneously, and aligns the deformed images in the way that the forward and backward transformations are inverse consistent. To avoid the direct computation of the inverse transformation fie...
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The aim of this work is to improve the accuracy, robustness and efficiency of the compressed sensing reconstruction technique in magnetic reso-nance imaging. We propose a novel variational model that enforces the sparsity of the underlying image in terms of its spatial finite differences and represen-tation with respect to a dictionary. The diction...
Article
We present a novel variational framework for deformable multi-modal image registration. Our approach is based on Renyi's statistical dependence measure of two random variables with the use of reproducing kernel Hilbert spaces associated with Gaussian kernels to simplify the computation. The popularly used method of maximizing mutual information bas...
Article
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Magnetization transfer imaging (MT) may have considerable promise for early detection and monitoring of subtle brain changes before they are apparent on conventional magnetic resonance images. At 3 Tesla (T), MT affords higher resolution and increased tissue contrast associated with macromolecules. The reliability and reproducibility of a new high-...
Conference Paper
This paper developed a new soft multiphase segmentation model. Different from most maximum-likelihood based and Bayesian-estimation based methods, the proposed model introduced a geometrical constraint- ¿the length term¿ into the model which makes the model more rigorous in analysis while still flexible in implementation. Moreover, the model used...
Conference Paper
This paper presents a novel variational model for inverse consistent deformable image registration. This model deforms the source and target image simultaneously, and aligns the deformed source and deformed target images in the way that the both transformations are inverse consistent. The model does not computes the inverse transforms explicitly, a...
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We present a novel variational formulation for restoring high angular resolution diffusion imaging (HARDI) data. The restoration formulation involves smoothing signal measurements over the spherical domain and across the 3D image lattice. The regularization across the lattice is achieved using a total variation (TV) norm based scheme, while the fin...
Conference Paper
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To reduce acquisition time in magnetic resonance (MR) imaging, compressive sensing and sparse representation techniques have been developed to reconstruct MR images with partially acquired data. Although this has been a hot research topic in the field, it has not been used clinically due to three inherent problems of its current framework: potentia...
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Bregman divergences have played an important role in many research areas. Diver-gence is a measure of dissimilarity and by itself not a metric. If a function of the divergence is a metric, then it becomes much more powerful. In Part 1 we have given necessary and sufficient condi-tions on the convex function in order that the square root of the aver...
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Bregman divergences are generalizations of the well known Kullback Leibler diver-gence. They are based on convex functions and have recently received great attention. We present a class of "squared root metrics" based on Bregman divergences. They can be regarded as natural generalization of Euclidean distance. We provide necessary and sufficient co...
Conference Paper
In this paper, we propose a segmentation assisted registration model. It partitions the domain of images into several regions such that the residue image in each region is identically distributed with zero mean and variance to be optimized. In this model, we minimize an energy that combines negative log-likelihood of the residue in each region, smo...
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Diffusion tensor magnetic resonance imaging (DT-MRI, shortened as DTI) produces, from a set of diffusion-weighted magnetic resonance images, tensor-valued images where each voxel is assigned a 3x3 symmetric, positive-definite matrix. This tensor is simply the covariance matrix of a local Gaussian process with zero mean, modelling the average motion...
Article
We present a novel classifier for a collection of nonnegative L1L1 functions. Given two sets of data, one set coming from “similar” distributions labeled as normal, and the other unspecified labeled as abnormal. To understand the structure of normality, and further to classify new data with minimal errors, we propose to find the smallest CKL sphere...
Conference Paper
This paper provides a novel algorithm for invertible non- rigid image registration. The proposed model minimizes two energy functionals coupled by a natural inverse consistent constraint. Both of the energy functionals for forward and backward deformation fields consist a smoothness measure of the deformation field, and a similarity measure between...
Article
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We present a new variational framework for simultaneous smooth- ing and estimation of apparent diffusion coefficient (ADC) profiles from High Angular Resolution Diffusion-weighted MRI. The model approximates the ADC profiles at each voxel by a 4th order spherical harmonic series (SHS). The co- efficients in SHS are obtained by solving a constrained...
Article
EM and level set algorithms are competing methods for segmenting MRI brain images. This paper presents a fair comparison of the two techniques using the Montreal Neurological Institute's software phantom. There are many flavors of level set algorithms for segmentation into multiple regions (multi-phase algorithms, multi-layer algorithms). The speci...
Conference Paper
Most living organisms develop the capacity of generating autonomously sustained oscillations with a period close to 24h. Genes have been thought to play a central role in the regulation of this process, but the detection of these genes (or quantitative trait loci, QTLs) has been made possible with a newly developed functional mapping model. Functio...
Conference Paper
In this paper, we propose a variational model for curve matching based on Kullback-Leibler(KL) divergence. This framework accomplishes the difficult task of finding correspondences for a group of curves simultaneously in a symmetric and transitive fashion. Moreover the distance in the energy functional has the metric property. We also introduce a l...
Conference Paper
The goal of this paper is to develop region based image segmentation algorithms. Two new variational PDE image segmentation models are proposed. The first model is obtained by minimizing an energy function w hich depends on a modified Mumford-Shah algorithm. The second model is acqui red by utilizing prior shape information and region intensity val...
Article
We present a coupled minimization problem for image segmentation using prior shape and intensity profile. One part of the model minimizes a shape related energy and the energy of geometric active contour with a parameter that balances the influence from these two. The minimizer corresponding to a fixed parameter in this minimization gives a segment...
Article
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Genes that control circadian rhythms in organisms have been recognized, but have been difficult to detect because circadian behavior comprises periodically dynamic traits and is sensitive to environmental changes. We present a statistical model for mapping and characterizing specific genes or quantitative trait loci (QTL) that affect variations in...
Chapter
We present a few more desirable properties to find correspondence and dissimilarity of two plane curves in nonrigid sense. A crossed scheme is used to define dissimilarity metric, which ensures an actual bi-morphism between two curves to be aligned. The optimal correspondence is found by a modified dynamic-programming method. From the optimal corre...
Chapter
A new algorithm for generating shape models using a Self-Organizing map (SOM) is presented. The aim of the model is to develop an approach for shape representation and classification to detect differences in the shape of anatomical structures. The Self-Organizing map requires specification of the number of clusters in advance, but does not depend u...
Chapter
We present a variational framework for determination of intra-voxel fiber orientations from High Angular Resolution Diffusion-Weighted (HARD) MRI under the assumption of biGaussian diffusion. The approach is simultaneously estimating and regularizing the two tensor fields and the field of the proportionality corresponding to the mixture of two Gaus...
Conference Paper
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The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. Diffusion weighted imaging (DWI) is a noninvasive technique that can visualize the neuronal projections connecting the functional centers and thus provides new keys to the understanding of brain function. In this paper, we assume there...
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RESUMEN RESUMEN We study a<sup> </sup> functional with variable exponent , $1 \leq p(x)\ leq 2$, which provides a<sup> </sup> model for image denoising , enhancement , and restoration . The diffusion resulting <sup> </sup> from the proposed model is a combination of total variation <sup> </sup>(TV)- based regularization and Gaussian smoothing . T...
Conference Paper
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We propose a novel variational approach to automatically soft-segment medical images into a fixed number of classes. Our method combines fuzzy classification and active contours in a single variational framework. This approach allows the use of tools from both de-formable geometry and clustering in a well-defined setting and provides a useful, unsu...
Conference Paper
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Piecewise constant and piecewise smooth Mumford-Shah (MS) models have been widely studied and used for image segmentation. More complicatedthanpiecewiseconstant MS, global Gaussian intensity distribution within each partitioned region has also been studied. However, all these frameworks are limited in power and robustness in finding objects whose i...
Conference Paper
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We have developed a geometric deformable model that employs neighborhood influence to achieve robust segmentation for noisy and broken edges. The fundamental power of this strategy rests with the explicitly combination of regional inter-point constraints, image forces, and a priori boundary information for each geometric contour point within its ad...
Conference Paper
We present two new algorithms for correspondence and classification of planar curves in a non-rigid sense. In the first algorithm we define deforming energy based on aligning curves using certain of their properties, namely Multi-Step-Size Local Similarity (MSSLS) and the difference between the angle changes of beginning and ending tangent lines of...
Chapter
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We present a novel variational model to find shape-based correspondences between two sets of level curves. While the usual correspondence techniques work with parametrized curves, we use a level-set formulation that enables us to handle curves with arbitrary topology. Given the functions F1: (W1 Í IR2) ® IR\Phi_{1}: (\Omega_{1} \subseteq IR^{2}) \l...
Conference Paper
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We present a novel level set representation and front propagation scheme for active contours where the analysis/evolution domain is sampled by unstructured point cloud. These sampling points are adaptively distributed according to both local data and level set geometry, hence allow extremely convenient enhancement/reduction of local front precision...
Conference Paper
Conventional level set based image segmentations are performed upon certain underlying grid/mesh structures for explicit spatial discretization of the problem and evolution domains. Such computational grids, however, lead to typically expensive and difficult grid refinement/remeshing problems whenever tradeoffs between time and precision are deemed...
Conference Paper
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A new variational PDE based level set method for a simultaneous image segmentation and non-rigid registration using prior shape and intensity information is presented. The segmentation is obtained by finding a non-rigid registration to the prior shape. The non-rigid registration consists of both a global rigid transformation and a local non-rigid d...
Conference Paper
While geometric deformable models have brought tremendous impacts on shape representation and analysis in medical image analysis, some of the remaining problems include the handling of boundary leakage and the lack of global understanding of boundaries. We present a modification to the geodesic active contour framework such that influence from loca...
Article
Inpainting is an image interpolation method. Partial differential equation (PDE)-based digital inpainting techniques are finding broad applications. In this paper, PDE-based inpainting techniques are applied to the field of MR parallel imaging. A novel model and its corresponding numerical method are introduced. This model is then applied to sensit...
Article
In this paper we study the partial regularity of a functional on BV space proposed by Chambolle and Lions (3) for the purposes of image restoration. The functional is designed to smooth corrupted images using isotropic diusion via the Laplacian where the gradients of the image are below a certain threshold and retain edges where gradients are above...
Article
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In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L(P) norms, subject to a nonlinear inequality constraint on the d...
Article
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In this paper, we use the cumulative distribution of a random variable to define its information content and thereby develop an alternative measure of uncertainty that extends Shannon entropy to random variables with continuous distributions. We call this measure cumulative residual entropy (CRE). The salient features of CRE are as follows: 1) it i...
Conference Paper
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We present a variational framework for determination of intra-voxel fiber orientations from High Angular Resolution Diffusion-Weighted (HARD) MRI under the assumption of biGaussian diffusion. The approach is simultaneously estimating and regularizing the two tensor fields and the field of the proportionality corresponding to the mixture of two Gaus...
Conference Paper
Full-text available
We present a new variational framework for recovery of apparent diffusion coefficient (ADC)from High Angular Resolution Diffusion-weighted (HARD) MRI. The model approximates the ADC profiles by a 4th order spherical harmonic series (SHS), whose coefficients are obtained by solving a constrained minimization problem. By minimizing the energy functio...
Conference Paper
In this paper, we develop a model for estimating the accuracy of a nonlinear estimator used in estimating the apparent diffusivity coefficient (ADC) which provides useful information about the structure of tissue being imaged with diffusion weighted MR. Further, we study the statistical properties of the nonlinear estimator and use them to design o...
Article
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We present a functional of nonstandard growth for which the corresponding minimization problem provides a model for image denoising, enhancement, and restoration. The diffusion resulting from the proposed model is a combination of isotropic and anisotropic diffusion. Isotropic diffusion is used at locations with low gradient and total variation bas...
Conference Paper
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In this note we present a coupled optimization model for boundary determination. One part of the model incorporates a prior shape into a geometric active contour model with a fixed parameter. The second part determines the 'best' parameter used in the first part by maximi