Biomedical Imaging Group
Biomedical Imaging Group
Storath M.,Universit& and x00E4 |
Rickert D.,Institute of Computational Biology |
Unser M.,Biomedical Imaging Group |
Weinmann A.,FH Darmstadt
IEEE Transactions on Image Processing | Year: 2017
We develop a fast algorithm for segmenting 3D images from linear measurements based on the Potts model (or piecewise constant Mumford-Shah model). To that end, we first derive suitable space discretizations of the 3D Potts model which are capable of dealing with 3D images defined on non-cubic grids. Our discretization allows us to utilize a specific splitting approach which results in decoupled subproblems of moderate size. The crucial point in the 3D setup is that the number of independent subproblems is so large that we can reasonably exploit the parallel processing capabilities of the graphics processing units (GPU). Our GPU implementation is up to 18 times faster than the sequential CPU version. This allows to process even large volumes in acceptable runtimes. As a further contribution, we extend the algorithm in order to deal with non-negativity constraints. We demonstrate the efficiency of our method for combined image deconvolution and segmentation on simulated data and on real 3D widefield fluorescence microscopy data. OAPA
Ducros N.,Polytechnic of Milan |
Ducros N.,French National Center for Scientific Research |
Ducros N.,French Institute of Health and Medical Research |
Ducros N.,University Claude Bernard Lyon 1 |
And 12 more authors.
Medical Physics | Year: 2010
Purpose: In the context of fluorescence diffuse optical tomography, determining the optimal way to exploit the time-resolved information has been receiving much attention and different features of the time-resolved signals have been introduced. In this article, the authors revisit and generalize the notion of feature, considering the projection of the measurements onto some basis functions. This leads the authors to propose a novel approach based on the wavelet transform of the measurements. Methods: A comparative study between the reconstructions obtained from the proposed wavelet-based approach and the reconstructions obtained from the reference temporal moments is provided. An inhomogeneous cubic medium is considered. Reconstructions are performed from synthetic measurements assuming Poisson noise statistics. In order to provide fairly comparable reconstructions, the reconstruction scheme is associated with a particular procedure for selecting the regularization parameter. Results: In the noise-free case, the reconstruction quality is shown to be mainly driven by the number of selected features. In the presence of noise, however, the reconstruction quality depends on the type of the features. In this case, the wavelet approach is shown to outperform the moment approach. While the optimal time-resolved reconstruction quality, which is obtained considering the whole set of time samples, is recovered using only eight wavelet functions, it cannot be attained using moments. It is finally observed that the time-resolved information is of limited utility, in terms of reconstruction, when the maximum number of detected photons is lower than 105. Conclusions: The wavelet approach allows for better exploiting the time-resolved information, especially when the number of detected photons is low. However, when the number of detected photons decreases below a certain threshold, the time-resolved information itself is shown to be of limited utility. © 2010 American Association of Physicists in Medicine.
Zhang C.-H.,Nanjing University |
Zhang C.-H.,University of Massachusetts Medical School |
Li Y.,Nanjing University |
Li Y.,University of Massachusetts Medical School |
And 8 more authors.
American Journal of Respiratory and Critical Care Medicine | Year: 2013
Rationale: Asthma is a chronic inflammatory disorder with a characteristic of airway hyperresponsiveness (AHR). Ca2+-activated Cl [Cl (Ca)] channels are inferred to be involved in AHR, yet their molecular nature and the cell type they act within to mediate this response remain unknown. Objectives: Transmembrane protein 16A (TMEM16A) and TMEM16B are Cl(Ca) channels,andactivation of Cl(Ca) channels in airwaysmooth muscle (ASM) contributes to agonist-induced airway contraction. We hypothesized that Tmem16a and/or Tmem16b encode Cl(Ca) channels in ASM and mediate AHR. Methods: We assessed the expression of the TMEM16 family, and the effects of niflumic acid and benzbromarone on AHR and airway contraction, in an ovalbumin-sensitized mouse model of chronic asthma. We also cloned TMEM16A from ASM and examined the Cl currents it produced in HEK293 cells. We further studied the impacts of TMEM16A deletion on Ca2+ agonist-induced cell shortening, and on Cl(Ca) currents activated by Ca2+ sparks (localized, short-lived Ca2+ transientsduetotheopeningof ryanodinereceptors)inmouseASMcells. Measurements and Main Results: TMEM16A, but not TMEM16B, is expressed in ASM cells and its expression in these cells is up-regulated in ovalbumin-sensitized mice. Niflumic acid and benzbromarone prevent AHR and contraction evoked by methacholine in ovalbuminsensitizedmice. TMEM16Aproduces Cl(Ca) currentswith kinetics similar tonativeCl(Ca) currents.TMEM16AdeletionrendersCa 2+ sparksunable to activate Cl(Ca) currents, and weakens caffeine- and methacholineinduced cell shortening. Conclusions: Tmem16a encodes Cl(Ca) channels in ASM and contributes toCa2+agonist- inducedcontraction. In addition, up-regulation of TMEM16A and its augmented activation contribute to AHR in an ovalbumin-sensitized mouse model of chronic asthma. TMEM16A may represent a potential therapeutic target for asthma. Copyright © 2013 by the American Thoracic Society.
Chaudhury K.N.,Princeton University |
Sage D.,Biomedical Imaging Group |
Unser M.,Biomedical Imaging Group
IEEE Transactions on Image Processing | Year: 2011
It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image-processing literature. Along with the spatial filter, the edge-preserving bilateral filter involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process nonlinear and computationally intensive, particularly when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant time, by using trigonometric range kernels. This is done by generalizing the idea presented by Porikli, i.e., using polynomial kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained by Porikli using polynomials. © 2006 IEEE.
Tohidi P.,Sharif University of Technology |
Bostan E.,Biomedical Imaging Group |
Pad P.,Biomedical Imaging Group |
Unser M.,Biomedical Imaging Group
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2016
We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is an iterative algorithm that combines standard wavelet-based thresholding with an optimized non-linearity and cycle-spinning. This method is more computationally efficient than the former and appears to provide the same optimal denoising results in practice. We illustrate the superior performance of both methods through numerical simulations by comparing them with other well-known denoising schemes. © 2016 IEEE.
PubMed | Biomedical Imaging Group and Erasmus University Rotterdam
Type: Journal Article | Journal: Journal of biomechanics | Year: 2013
Heterogeneity in plaque composition in human coronary artery bifurcations is associated with blood flow induced shear stress. Shear stress is generally determined by combing 3D lumen data and computational fluid dynamics (CFD). We investigated two new procedures to generate 3D lumen reconstructions of coronary artery bifurcations for shear stress computations.We imaged 10 patients with multislice computer tomography (MSCT) and intravascular ultrasound (IVUS). The 3D reconstruction of the main branch was based on the fusion of MSCT and IVUS. The proximal part of side branch was reconstructed using IVUS data or MSCT data, resulting in two different reconstructions of the bifurcation region. The distal part of the side branch was based on MSCT data alone. The reconstructed lumen was combined with CFD to determine the shear stress. Low and high shear stress regions were defined and shear stress patterns in the bifurcation regions were investigated.The 3D coronary bifurcations were successfully generated with both reconstruction procedures. The geometrical features of the bifurcation region for the two reconstruction procedures did not reveal appreciable differences. The shear stress maps showed a qualitative agreement, and the low and high shear stress regions were similar in size and average shear stress values were identical. The low and high shear stress regions showed an overlap of approximately 75%.Reconstruction of the side branch with MSCT data alone is an adequate technique to study shear stress and wall thickness in the bifurcation region. The reconstruction procedure can be applied to further investigate the effect of shear stress on atherosclerosis in coronary bifurcations.
Lefkimmiatis S.,Biomedical Imaging Group |
Unser M.,Biomedical Imaging Group
IEEE Transactions on Image Processing | Year: 2013
Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupted by Poisson noise. In this paper, we propose an efficient framework for Poisson image reconstruction, under a regularization approach, which depends on matrix-valued regularization operators. In particular, the employed regularizers involve the Hessian as the regularization operator and Schatten matrix norms as the potential functions. For the solution of the problem, we propose two optimization algorithms that are specifically tailored to the Poisson nature of the noise. These algorithms are based on an augmented-Lagrangian formulation of the problem and correspond to two variants of the alternating direction method of multipliers. Further, we derive a link that relates the proximal map of an $\ellp norm with the proximal map of a Schatten matrix norm of order $p$. This link plays a key role in the development of one of the proposed algorithms. Finally, we provide experimental results on natural and biological images for the task of Poisson image deblurring and demonstrate the practical relevance and effectiveness of the proposed framework. © 2013 IEEE.