Electron paramagnetic resonance image reconstruction with total variation and curvelets regularization

Science.gov (United States)

Durand, Sylvain; Frapart, Yves-Michel; Kerebel, Maud

2017-11-01

Spatial electron paramagnetic resonance imaging (EPRI) is a recent method to localize and characterize free radicals in vivo or in vitro, leading to applications in material and biomedical sciences. To improve the quality of the reconstruction obtained by EPRI, a variational method is proposed to inverse the image formation model. It is based on a least-square data-fidelity term and the total variation and Besov seminorm for the regularization term. To fully comprehend the Besov seminorm, an implementation using the curvelet transform and the L 1 norm enforcing the sparsity is proposed. It allows our model to reconstruct both image where acquisition information are missing and image with details in textured areas, thus opening possibilities to reduce acquisition times. To implement the minimization problem using the algorithm developed by Chambolle and Pock, a thorough analysis of the direct model is undertaken and the latter is inverted while avoiding the use of filtered backprojection (FBP) and of non-uniform Fourier transform. Numerical experiments are carried out on simulated data, where the proposed model outperforms both visually and quantitatively the classical model using deconvolution and FBP. Improved reconstructions on real data, acquired on an irradiated distal phalanx, were successfully obtained.

A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction.

Science.gov (United States)

Chen, Bo; Bian, Zhaoying; Zhou, Xiaohui; Chen, Wensheng; Ma, Jianhua; Liang, Zhengrong

2018-04-12

Total variation (TV) minimization for the sparse-view x-ray computer tomography (CT) reconstruction has been widely explored to reduce radiation dose. However, due to the piecewise constant assumption for the TV model, the reconstructed images often suffer from over-smoothness on the image edges. To mitigate this drawback of TV minimization, we present a Mumford-Shah total variation (MSTV) minimization algorithm in this paper. The presented MSTV model is derived by integrating TV minimization and Mumford-Shah segmentation. Subsequently, a penalized weighted least-squares (PWLS) scheme with MSTV is developed for the sparse-view CT reconstruction. For simplicity, the proposed algorithm is named as 'PWLS-MSTV.' To evaluate the performance of the present PWLS-MSTV algorithm, both qualitative and quantitative studies were conducted by using a digital XCAT phantom and a physical phantom. Experimental results show that the present PWLS-MSTV algorithm has noticeable gains over the existing algorithms in terms of noise reduction, contrast-to-ratio measure and edge-preservation.

Combining variational and model-based techniques to register PET and MR images in hand osteoarthritis

International Nuclear Information System (INIS)

Magee, Derek; Tanner, Steven F; Jeavons, Alan P; Waller, Michael; Tan, Ai Lyn; McGonagle, Dennis

2010-01-01

Co-registration of clinical images acquired using different imaging modalities and equipment is finding increasing use in patient studies. Here we present a method for registering high-resolution positron emission tomography (PET) data of the hand acquired using high-density avalanche chambers with magnetic resonance (MR) images of the finger obtained using a 'microscopy coil'. This allows the identification of the anatomical location of the PET radiotracer and thereby locates areas of active bone metabolism/'turnover'. Image fusion involving data acquired from the hand is demanding because rigid-body transformations cannot be employed to accurately register the images. The non-rigid registration technique that has been implemented in this study uses a variational approach to maximize the mutual information between images acquired using these different imaging modalities. A piecewise model of the fingers is employed to ensure that the methodology is robust and that it generates an accurate registration. Evaluation of the accuracy of the technique is tested using both synthetic data and PET and MR images acquired from patients with osteoarthritis. The method outperforms some established non-rigid registration techniques and results in a mean registration error that is less than approximately 1.5 mm in the vicinity of the finger joints.

Fractional-Order Total Variation Image Restoration Based on Primal-Dual Algorithm

OpenAIRE

Chen, Dali; Chen, YangQuan; Xue, Dingyu

2013-01-01

This paper proposes a fractional-order total variation image denoising algorithm based on the primal-dual method, which provides a much more elegant and effective way of treating problems of the algorithm implementation, ill-posed inverse, convergence rate, and blocky effect. The fractional-order total variation model is introduced by generalizing the first-order model, and the corresponding saddle-point and dual formulation are constructed in theory. In order to guarantee $O(1/{N}^{2})$ conv...

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

Science.gov (United States)

Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

2017-12-01

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Upper mantle compositional variations and discontinuity topography imaged beneath Australia from Bayesian inversion of surface-wave phase velocities and thermochemical modeling

DEFF Research Database (Denmark)

Khan, A.; Zunino, Andrea; Deschamps, F.

2013-01-01

Here we discuss the nature of velocity heterogeneities seen in seismic tomography images of Earth's mantle whose origins and relation to thermochemical variations are yet to be understood. We illustrate this by inverting fundamental-mode and higher-order surface-wave phase velocities for radial....../Fe and Mg/Si values relative to surrounding mantle. Correlated herewith are thermal variations that closely follow surface tectonics. We also observe a strong contribution to lateral variations in structure and topography across the “410 km” seismic discontinuity from thermochemically induced phase......-wave tomography models with other regional models is encouraging. Radial anisotropy is strongest at 150/200 km depth beneath oceanic/continental areas, respectively, and appears weak and homogeneous below. Finally, geoid anomalies are computed for a subset of sampled model and compared to observations....

Positive and negative variations in capacitive images for given defects under varying experimental conditions

Science.gov (United States)

Li, Chen; Yin, Xiaokang; Li, Zhen; Li, Wei; Chen, Guoming

2018-04-01

Capacitive imaging (CI) technique is a novel electromagnetic NDE technique. The Quasi-static electromagnetic field from the carefully designed electrode pair will vary when the electrical properties of the sample change, leading to the possibility of imaging. It is observed that for a given specimen, the targeted features appear as different variations in capacitive images under different experimental conditions. In some cases, even opposite variations occur, which brings confusion to indication interpretation. It is thus thought interesting to embark on investigations into the cause and effects of the negative variation phenomenon. In this work, the positive and negative variations were first explained from the measurement sensitivity distribution perspective. This was then followed by a detailed analysis using finite element models in COMSOL. A parametric experimental study on a glass fiber composite plate with artificial defects was then carried out to investigate how the experimental conditions affect the variation.

Learning-based stochastic object models for characterizing anatomical variations

Science.gov (United States)

Dolly, Steven R.; Lou, Yang; Anastasio, Mark A.; Li, Hua

2018-03-01

It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.

Bilinear models for inter- and intra-patient variation of the prostate

International Nuclear Information System (INIS)

Jeong, Y; Radke, R J; Lovelock, D M

2010-01-01

We propose bilinear models for capturing and effectively decoupling the expected shape variations of an organ both across the patient population and within a specific patient. Bilinear models have been successfully introduced in other areas of computer vision, but they have rarely been used in medical imaging applications. Our particular interest is in modeling the shape variation of the prostate for potential use in radiation therapy treatment planning. Using a dataset of 204 prostate shapes contoured from CT imagery of 12 different patients, we build bilinear models and show that they can fit both training and testing shapes accurately. We also show how the bilinear model can adapt to a new patient using only a few example shapes, producing a patient-specific model that also reflects expected content variation learnt from a broader population. Finally, we evaluate the training and testing projection error, adaptation performance and image segmentation accuracy of the bilinear model compared to linear principal component analysis and hierarchical point distribution models with the same number of parameters.

Bayesian Image Restoration Using a Large-Scale Total Patch Variation Prior

Directory of Open Access Journals (Sweden)

Yang Chen

2011-01-01

Full Text Available Edge-preserving Bayesian restorations using nonquadratic priors are often inefficient in restoring continuous variations and tend to produce block artifacts around edges in ill-posed inverse image restorations. To overcome this, we have proposed a spatial adaptive (SA prior with improved performance. However, this SA prior restoration suffers from high computational cost and the unguaranteed convergence problem. Concerning these issues, this paper proposes a Large-scale Total Patch Variation (LS-TPV Prior model for Bayesian image restoration. In this model, the prior for each pixel is defined as a singleton conditional probability, which is in a mixture prior form of one patch similarity prior and one weight entropy prior. A joint MAP estimation is thus built to ensure the iteration monotonicity. The intensive calculation of patch distances is greatly alleviated by the parallelization of Compute Unified Device Architecture(CUDA. Experiments with both simulated and real data validate the good performance of the proposed restoration.

A Fast Alternating Minimization Algorithm for Nonlocal Vectorial Total Variational Multichannel Image Denoising

Directory of Open Access Journals (Sweden)

Rubing Xi

2014-01-01

Full Text Available The variational models with nonlocal regularization offer superior image restoration quality over traditional method. But the processing speed remains a bottleneck due to the calculation quantity brought by the recent iterative algorithms. In this paper, a fast algorithm is proposed to restore the multichannel image in the presence of additive Gaussian noise by minimizing an energy function consisting of an l2-norm fidelity term and a nonlocal vectorial total variational regularization term. This algorithm is based on the variable splitting and penalty techniques in optimization. Following our previous work on the proof of the existence and the uniqueness of the solution of the model, we establish and prove the convergence properties of this algorithm, which are the finite convergence for some variables and the q-linear convergence for the rest. Experiments show that this model has a fabulous texture-preserving property in restoring color images. Both the theoretical derivation of the computation complexity analysis and the experimental results show that the proposed algorithm performs favorably in comparison to the widely used fixed point algorithm.

Variational Level Set Method for Two-Stage Image Segmentation Based on Morphological Gradients

Directory of Open Access Journals (Sweden)

Zemin Ren

2014-01-01

Full Text Available We use variational level set method and transition region extraction techniques to achieve image segmentation task. The proposed scheme is done by two steps. We first develop a novel algorithm to extract transition region based on the morphological gradient. After this, we integrate the transition region into a variational level set framework and develop a novel geometric active contour model, which include an external energy based on transition region and fractional order edge indicator function. The external energy is used to drive the zero level set toward the desired image features, such as object boundaries. Due to this external energy, the proposed model allows for more flexible initialization. The fractional order edge indicator function is incorporated into the length regularization term to diminish the influence of noise. Moreover, internal energy is added into the proposed model to penalize the deviation of the level set function from a signed distance function. The results evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed model has been applied to both synthetic and real images with promising results.

Variational PDE Models in Image Processing

National Research Council Canada - National Science Library

Chan, Tony F; Shen, Jianhong; Vese, Luminita

2002-01-01

.... These include astronomy and aerospace exploration, medical imaging, molecular imaging, computer graphics, human and machine vision, telecommunication, auto-piloting, surveillance video, and biometric...

Regional variation in Medicare payments for medical imaging: radiologists versus nonradiologists.

Science.gov (United States)

Rosman, David A; Nsiah, Eugene; Hughes, Danny R; Duszak, Richard

2015-05-01

The purpose of this article was to study regional variation in Medicare Physician Fee Schedule (MPFS) payments for medical imaging to radiologists compared with nonradiologists. Using a 5% random sample of all Medicare enrollees, which covered approximately 2.5 million Part B beneficiaries in 2011, total professional-only, technical-only, and global MPFS spending was calculated on a state-by-state and United States Census Bureau regional basis for all Medicare Berenson-Eggers Type of Service-defined medical imaging services. Payments to radiologists versus nonradiologists were identified and variation was analyzed. Nationally, mean MPFS medical imaging spending per Medicare beneficiary was $207.17 ($95.71 [46.2%] to radiologists vs $111.46 [53.8%] to nonradiologists). Of professional-only (typically interpretation) payments, 20.6% went to nonradiologists. Of technical-only (typically owned equipment) payments, 84.9% went to nonradiologists. Of global (both professional and technical) payments, 70.1% went to nonradiologists. The percentage of MPFS medical imaging spending on nonradiologists ranged from 32% (Minnesota) to 69.5% (South Carolina). The percentage of MPFS payments for medical imaging to nonradiologists exceeded those to radiologists in 58.8% of states. The relative percentage of MPFS payments to nonradiologists was highest in the South (58.5%) and lowest in the Northeast (48.0%). Nationally, 53.8% of MPFS payments for medical imaging services are made to nonradiologists, who claim a majority of MPFS payments in most states dominated by noninterpretive payments. This majority spending on nonradiologists may have implications in bundled and capitated payment models for radiology services. Medical imaging payment policy initiatives must consider the roles of all provider groups and associated regional variation.

Unified Probabilistic Models for Face Recognition from a Single Example Image per Person

Institute of Scientific and Technical Information of China (English)

Pin Liao; Li Shen

2004-01-01

This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach for face recognition.

Parallel algorithm of real-time infrared image restoration based on total variation theory

Science.gov (United States)

Zhu, Ran; Li, Miao; Long, Yunli; Zeng, Yaoyuan; An, Wei

2015-10-01

Image restoration is a necessary preprocessing step for infrared remote sensing applications. Traditional methods allow us to remove the noise but penalize too much the gradients corresponding to edges. Image restoration techniques based on variational approaches can solve this over-smoothing problem for the merits of their well-defined mathematical modeling of the restore procedure. The total variation (TV) of infrared image is introduced as a L1 regularization term added to the objective energy functional. It converts the restoration process to an optimization problem of functional involving a fidelity term to the image data plus a regularization term. Infrared image restoration technology with TV-L1 model exploits the remote sensing data obtained sufficiently and preserves information at edges caused by clouds. Numerical implementation algorithm is presented in detail. Analysis indicates that the structure of this algorithm can be easily implemented in parallelization. Therefore a parallel implementation of the TV-L1 filter based on multicore architecture with shared memory is proposed for infrared real-time remote sensing systems. Massive computation of image data is performed in parallel by cooperating threads running simultaneously on multiple cores. Several groups of synthetic infrared image data are used to validate the feasibility and effectiveness of the proposed parallel algorithm. Quantitative analysis of measuring the restored image quality compared to input image is presented. Experiment results show that the TV-L1 filter can restore the varying background image reasonably, and that its performance can achieve the requirement of real-time image processing.

General filtering method for electronic speckle pattern interferometry fringe images with various densities based on variational image decomposition.

Science.gov (United States)

Li, Biyuan; Tang, Chen; Gao, Guannan; Chen, Mingming; Tang, Shuwei; Lei, Zhenkun

2017-06-01

Filtering off speckle noise from a fringe image is one of the key tasks in electronic speckle pattern interferometry (ESPI). In general, ESPI fringe images can be divided into three categories: low-density fringe images, high-density fringe images, and variable-density fringe images. In this paper, we first present a general filtering method based on variational image decomposition that can filter speckle noise for ESPI fringe images with various densities. In our method, a variable-density ESPI fringe image is decomposed into low-density fringes, high-density fringes, and noise. A low-density fringe image is decomposed into low-density fringes and noise. A high-density fringe image is decomposed into high-density fringes and noise. We give some suitable function spaces to describe low-density fringes, high-density fringes, and noise, respectively. Then we construct several models and numerical algorithms for ESPI fringe images with various densities. And we investigate the performance of these models via our extensive experiments. Finally, we compare our proposed models with the windowed Fourier transform method and coherence enhancing diffusion partial differential equation filter. These two methods may be the most effective filtering methods at present. Furthermore, we use the proposed method to filter a collection of the experimentally obtained ESPI fringe images with poor quality. The experimental results demonstrate the performance of our proposed method.

Feasibility Study of Ex Ovo Chick Chorioallantoic Artery Model for Investigating Pulsatile Variation of Arterial Geometry.

Directory of Open Access Journals (Sweden)

Kweon-Ho Nam

Full Text Available Despite considerable research efforts on the relationship between arterial geometry and cardiovascular pathology, information is lacking on the pulsatile geometrical variation caused by arterial distensibility and cardiomotility because of the lack of suitable in vivo experimental models and the methodological difficulties in examining the arterial dynamics. We aimed to investigate the feasibility of using a chick embryo system as an experimental model for basic research on the pulsatile variation of arterial geometry. Optical microscope video images of various arterial shapes in chick chorioallantoic circulation were recorded from different locations and different embryo samples. The high optical transparency of the chorioallantoic membrane (CAM allowed clear observation of tiny vessels and their movements. Systolic and diastolic changes in arterial geometry were visualized by detecting the wall boundaries from binary images. Several to hundreds of microns of wall displacement variations were recognized during a pulsatile cycle. The spatial maps of the wall motion harmonics and magnitude ratio of harmonic components were obtained by analyzing the temporal brightness variation at each pixel in sequential grayscale images using spectral analysis techniques. The local variations in the spectral characteristics of the arterial wall motion were reflected well in the analysis results. In addition, mapping the phase angle of the fundamental frequency identified the regional variations in the wall motion directivity and phase shift. Regional variations in wall motion phase angle and fundamental-to-second harmonic ratio were remarkable near the bifurcation area. In summary, wall motion in various arterial geometry including straight, curved and bifurcated shapes was well observed in the CAM artery model, and their local and cyclic variations could be characterized by Fourier and wavelet transforms of the acquired video images. The CAM artery model with

Naturalness and image quality : chroma and hue variation in color images of natural scenes

NARCIS (Netherlands)

Ridder, de H.; Blommaert, F.J.J.; Fedorovskaya, E.A.; Rogowitz, B.E.; Allebach, J.P.

1995-01-01

The relation between perceptual image quality and naturalness was investigated by varying the colorfulness and hue of color images of natural scenes. These variations were created by digitizing the images, subsequently determining their color point distributions in the CIELUV color space and finally

Naturalness and image quality: Chroma and hue variation in color images of natural scenes

NARCIS (Netherlands)

Ridder, de H.; Blommaert, F.J.J.; Fedorovskaya, E.A.; Eschbach, R.; Braun, K.

1997-01-01

The relation between perceptual image quality and natural ness was investigated by varying the colorfulness and hue of color images of natural scenes. These variations were created by digitizing the images, subsequently determining their color point distributions in the CIELUV color space and

Diagnostic difficulties resulting from morphological image variation in spondylodiscitis MR imaging

International Nuclear Information System (INIS)

Dziurzyńska-Białek, Ewa; Kruk-Bachonko, Joanna; Guz, Wiesław; Łosicki, Marek; Krupski, Witold

2012-01-01

Spinal infection (discitis; spondylodiscitis) presents a wide spectrum of pathologies. The method of choice for spondylodiscitis imaging is magnetic resonance (MR). It provides detailed anatomical information, especially concerning epidural space and spinal cord. The main aim of this article is the description and evaluation of spondylodiscitis morphological variation visible in magnetic resonance imaging. In this article we retrospectively analysed the patients diagnosed at the Department of Radiology of the Provincial Hospital No 2 in Rzeszów between October 2009 and October 2011. The subjects involved a group of five women aged 41–74 (mean 56.3 years) and eight men aged 46–69 (mean 61,3 years). All patients had spondylodiscitis symptoms. All patients underwent MRI examination before and after the contrast enhancement. In three patients additional CT examination was performed. Following the MRI procedure all patients were diagnosed with typical symptoms of spondylodiscitis. It also revealed a number of pathologies resulting from morphological spondylodiscitis variation. Other pathologies found on the MR images of the study group patients involved epidural intra-canal spinal pathological masses causing spinal cord compression, lung abscess, pyothorax, paravertebral abscesses and epidural empyemas, abscess between adjacent vertebral bodies, abscesses beneath anterior longitudinal ligament, and iliopsoas muscle abscesses. In all cases a destruction of vertebral bodies with end plates loss restriction and cortical layer discontinuity was observed. Moreover, one person was diagnosed with pathological vertebral body fractures and liquefactive necrosis of the vertebral body. Spondylodiscitis manifests itself in a great number of morphological variations visible on the radiological images. Apart from ordinary features of vertebral bodies and discs, progressive spinal destruction is observed together with reactive bone changes and soft tissue infiltration. The latter

A new level set model for cell image segmentation

Science.gov (United States)

Ma, Jing-Feng; Hou, Kai; Bao, Shang-Lian; Chen, Chun

2011-02-01

In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.

Motion estimation of tagged cardiac magnetic resonance images using variational techniques

Czech Academy of Sciences Publication Activity Database

Carranza-Herrezuelo, N.; Bajo, A.; Šroubek, Filip; Santamarta, C.; Cristóbal, G.; Santos, A.; Ledesma-Carbayo, M.J.

2010-01-01

Roč. 34, č. 6 (2010), s. 514-522 ISSN 0895-6111 Institutional research plan: CEZ:AV0Z10750506 Keywords : medical imaging processing * motion estimation * variational techniques * tagged cardiac magnetic resonance images * optical flow Subject RIV: JD - Computer Applications, Robotics Impact factor: 1.110, year: 2010 http://library.utia.cas.cz/separaty/2010/ZOI/sroubek- motion estimation of tagged cardiac magnetic resonance images using variational techniques.pdf

Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction.

Science.gov (United States)

Zhang, Hanming; Wang, Linyuan; Yan, Bin; Li, Lei; Cai, Ailong; Hu, Guoen

2016-01-01

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of unfavorable staircase effects. However, conventional TGV regularization employs l1-based form, which is not the most direct method for maximizing sparsity prior. In this study, we propose a total generalized p-variation (TGpV) regularization model to improve the sparsity exploitation of TGV and offer efficient solutions to few-view CT image reconstruction problems. To solve the nonconvex optimization problem of the TGpV minimization model, we then present an efficient iterative algorithm based on the alternating minimization of augmented Lagrangian function. All of the resulting subproblems decoupled by variable splitting admit explicit solutions by applying alternating minimization method and generalized p-shrinkage mapping. In addition, approximate solutions that can be easily performed and quickly calculated through fast Fourier transform are derived using the proximal point method to reduce the cost of inner subproblems. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to validate the efficiency and feasibility of the proposed method. Overall, the proposed method exhibits reasonable performance and outperforms the original TGV-based method when applied to few-view problems.

Adaptive Proximal Point Algorithms for Total Variation Image Restoration

Directory of Open Access Journals (Sweden)

Ying Chen

2015-02-01

Full Text Available Image restoration is a fundamental problem in various areas of imaging sciences. This paper presents a class of adaptive proximal point algorithms (APPA with contraction strategy for total variational image restoration. In each iteration, the proposed methods choose an adaptive proximal parameter matrix which is not necessary symmetric. In fact, there is an inner extrapolation in the prediction step, which is followed by a correction step for contraction. And the inner extrapolation is implemented by an adaptive scheme. By using the framework of contraction method, global convergence result and a convergence rate of O(1/N could be established for the proposed methods. Numerical results are reported to illustrate the efficiency of the APPA methods for solving total variation image restoration problems. Comparisons with the state-of-the-art algorithms demonstrate that the proposed methods are comparable and promising.

New second order Mumford-Shah model based on Γ-convergence approximation for image processing

Science.gov (United States)

Duan, Jinming; Lu, Wenqi; Pan, Zhenkuan; Bai, Li

2016-05-01

In this paper, a second order variational model named the Mumford-Shah total generalized variation (MSTGV) is proposed for simultaneously image denoising and segmentation, which combines the original Γ-convergence approximated Mumford-Shah model with the second order total generalized variation (TGV). For image denoising, the proposed MSTGV can eliminate both the staircase artefact associated with the first order total variation and the edge blurring effect associated with the quadratic H1 regularization or the second order bounded Hessian regularization. For image segmentation, the MSTGV can obtain clear and continuous boundaries of objects in the image. To improve computational efficiency, the implementation of the MSTGV does not directly solve its high order nonlinear partial differential equations and instead exploits the efficient split Bregman algorithm. The algorithm benefits from the fast Fourier transform, analytical generalized soft thresholding equation, and Gauss-Seidel iteration. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed model.

A new level set model for cell image segmentation

International Nuclear Information System (INIS)

Ma Jing-Feng; Chen Chun; Hou Kai; Bao Shang-Lian

2011-01-01

In this paper we first determine three phases of cell images: background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing. (cross-disciplinary physics and related areas of science and technology)

An algorithm for total variation regularized photoacoustic imaging

DEFF Research Database (Denmark)

Dong, Yiqiu; Görner, Torsten; Kunis, Stefan

2014-01-01

Recovery of image data from photoacoustic measurements asks for the inversion of the spherical mean value operator. In contrast to direct inversion methods for specific geometries, we consider a semismooth Newton scheme to solve a total variation regularized least squares problem. During the iter......Recovery of image data from photoacoustic measurements asks for the inversion of the spherical mean value operator. In contrast to direct inversion methods for specific geometries, we consider a semismooth Newton scheme to solve a total variation regularized least squares problem. During...... the iteration, each matrix vector multiplication is realized in an efficient way using a recently proposed spectral discretization of the spherical mean value operator. All theoretical results are illustrated by numerical experiments....

Research on compressive sensing reconstruction algorithm based on total variation model

Science.gov (United States)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

State Variation in Medical Imaging: Despite Great Variation, the Medicare Spending Decline Continues.

Science.gov (United States)

Rosenkrantz, Andrew B; Hughes, Danny R; Duszak, Richard

2015-10-01

The purpose of this study was to assess state-level trends in per beneficiary Medicare spending on medical imaging. Medicare part B 5% research identifiable files from 2004 through 2012 were used to compute national and state-by-state annual average per beneficiary spending on imaging. State-to-state geographic variation and temporal trends were analyzed. National average per beneficiary Medicare part B spending on imaging increased 7.8% annually between 2004 ($350.54) and its peak in 2006 ($405.41) then decreased 4.4% annually between 2006 and 2012 ($298.63). In 2012, annual per beneficiary spending was highest in Florida ($367.25) and New York ($355.67) and lowest in Ohio ($67.08) and Vermont ($72.78). Maximum state-to-state geographic variation increased over time, with the ratio of highest-spending state to lowest-spending state increasing from 4.0 in 2004 to 5.5 in 2012. Spending in nearly all states decreased since peaks in 2005 (six states) or 2006 (43 states). The average annual decrease among states was 5.1% ± 1.8% (range, 1.2-12.2%) The largest decrease was in Ohio. In only two states did per beneficiary spending increase (Maryland, 12.5% average annual increase since 2005; Oregon, 4.8% average annual increase since 2008). Medicare part B average per beneficiary spending on medical imaging declined in nearly every state since 2005 and 2006 peaks, abruptly reversing previously reported trends. Spending continued to increase, however, in Maryland and Oregon. Identification of state-level variation may facilitate future investigation of the potential effect of specific and regional changes in spending on patient access and outcomes.

Identification and Modelling of the In-Plane Reinforcement Orientation Variations in a CFRP Laminate Produced by Manual Lay-Up

Science.gov (United States)

Davila, Yves; Crouzeix, Laurent; Douchin, Bernard; Collombet, Francis; Grunevald, Yves-Henri

2017-08-01

Reinforcement angle orientation has a significant effect on the mechanical properties of composite materials. This work presents a methodology to introduce variable reinforcement angles into finite element (FE) models of composite structures. The study of reinforcement orientation variations uses meta-models to identify and control a continuous variation across the composite ply. First, the reinforcement angle is measured through image analysis techniques of the composite plies during the lay-up phase. Image analysis results show that variations in the mean ply orientations are between -0.5 and 0.5° with standard deviations ranging between 0.34 and 0.41°. An automatic post-treatment of the images determines the global and local angle variations yielding good agreements visually and numerically between the analysed images and the identified parameters. A composite plate analysed at the end of the cooling phase is presented as a case of study. Here, the variation in residual strains induced by the variability in the reinforcement orientation are up to 28% of the strain field of the homogeneous FE model. The proposed methodology has shown its capabilities to introduce material and geometrical variability into FE analysis of layered composite structures.

Identification and Modelling of the In-Plane Reinforcement Orientation Variations in a CFRP Laminate Produced by Manual Lay-Up

Science.gov (United States)

Davila, Yves; Crouzeix, Laurent; Douchin, Bernard; Collombet, Francis; Grunevald, Yves-Henri

2018-06-01

Reinforcement angle orientation has a significant effect on the mechanical properties of composite materials. This work presents a methodology to introduce variable reinforcement angles into finite element (FE) models of composite structures. The study of reinforcement orientation variations uses meta-models to identify and control a continuous variation across the composite ply. First, the reinforcement angle is measured through image analysis techniques of the composite plies during the lay-up phase. Image analysis results show that variations in the mean ply orientations are between -0.5 and 0.5° with standard deviations ranging between 0.34 and 0.41°. An automatic post-treatment of the images determines the global and local angle variations yielding good agreements visually and numerically between the analysed images and the identified parameters. A composite plate analysed at the end of the cooling phase is presented as a case of study. Here, the variation in residual strains induced by the variability in the reinforcement orientation are up to 28% of the strain field of the homogeneous FE model. The proposed methodology has shown its capabilities to introduce material and geometrical variability into FE analysis of layered composite structures.

Efficient Variational Approaches for Deformable Registration of Images

Directory of Open Access Journals (Sweden)

Mehmet Ali Akinlar

2012-01-01

Full Text Available Dirichlet, anisotropic, and Huber regularization terms are presented for efficient registration of deformable images. Image registration, an ill-posed optimization problem, is solved using a gradient-descent-based method and some fundamental theorems in calculus of variations. Euler-Lagrange equations with homogeneous Neumann boundary conditions are obtained. These equations are discretized by multigrid and finite difference numerical techniques. The method is applied to the registration of brain MR images of size 65×65. Computational results indicate that the presented method is quite fast and efficient in the registration of deformable medical images.

Inter-observer variation in masked and unmasked images for quality evaluation of clinical radiographs

International Nuclear Information System (INIS)

Tingberg, A.; Eriksson, F.; Medin, J.; Besjakov, J.; Baarth, M.; Haakansson, M.; Sandborg, M.; Almen, A.; Lanhede, B.; Alm-Carlsson, G.; Mattsson, S.; Maansson, L. G.

2005-01-01

Purpose: To investigate the influence of masking on the inter-observer variation in image quality evaluation of clinical radiographs of chest and lumbar spine. Background: Inter-observer variation is a big problem in image quality evaluation since this variation is often much bigger than the variation in image quality between, for example, two radiographic systems. In this study, we have evaluated the effect of masking on the inter-observer variation. The idea of the masking was to force every observer to view exactly the same part of the image and to avoid the effect of the overall 'first impression' of the image. A discussion with a group of European expert radiologists before the study indicated that masking might be a good way to reduce the inter-observer variation. Methods: Five chest and five lumbar spine radiographs were collected together with detailed information regarding exposure conditions. The radiographs were digitised with a high-performance scanner and five different manipulations were performed, simulating five different exposure conditions. The contrast, noise and spatial resolution were manipulated by this method. The images were printed onto the film and the individual masks were produced for each film, showing only the parts of the images that were necessary for the image quality evaluation. The quality of the images was evaluated on ordinary viewing boxes by a large group of experienced radiologists. The images were examined with and without the masks with a set of image criteria (if fulfilled, 1 point; and not fulfilled, 0 point), and the mean score was calculated for each simulated exposure condition. Results: The results of this study indicate that - contrary to what was supposed - the inter-observer variation increased when the images were masked. In some cases, especially for chest, this increase was statistically significant. Conclusions: Based on the results of this study, image masking in studies of fulfilment of image criteria cannot

4D segmentation of brain MR images with constrained cortical thickness variation.

Directory of Open Access Journals (Sweden)

Li Wang

Full Text Available Segmentation of brain MR images plays an important role in longitudinal investigation of developmental, aging, disease progression changes in the cerebral cortex. However, most existing brain segmentation methods consider multiple time-point images individually and thus cannot achieve longitudinal consistency. For example, cortical thickness measured from the segmented image will contain unnecessary temporal variations, which will affect the time related change pattern and eventually reduce the statistical power of analysis. In this paper, we propose a 4D segmentation framework for the adult brain MR images with the constraint of cortical thickness variations. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness being within a reasonable range, and temporal cortical thickness variation constraint in neighboring time-points to suppress the artificial variations. The proposed method has been tested on BLSA dataset and ADNI dataset with promising results. Both qualitative and quantitative experimental results demonstrate the advantage of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.

A Variational Approach to Simultaneous Image Segmentation and Bias Correction.

Science.gov (United States)

Zhang, Kaihua; Liu, Qingshan; Song, Huihui; Li, Xuelong

2015-08-01

This paper presents a novel variational approach for simultaneous estimation of bias field and segmentation of images with intensity inhomogeneity. We model intensity of inhomogeneous objects to be Gaussian distributed with different means and variances, and then introduce a sliding window to map the original image intensity onto another domain, where the intensity distribution of each object is still Gaussian but can be better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying the bias field with a piecewise constant signal within the sliding window. A maximum likelihood energy functional is then defined on each local region, which combines the bias field, the membership function of the object region, and the constant approximating the true signal from its corresponding object. The energy functional is then extended to the whole image domain by the Bayesian learning approach. An efficient iterative algorithm is proposed for energy minimization, via which the image segmentation and bias field correction are simultaneously achieved. Furthermore, the smoothness of the obtained optimal bias field is ensured by the normalized convolutions without extra cost. Experiments on real images demonstrated the superiority of the proposed algorithm to other state-of-the-art representative methods.

A population based statistical model for daily geometric variations in the thorax

NARCIS (Netherlands)

Szeto, Yenny Z.; Witte, Marnix G.; van Herk, Marcel; Sonke, Jan-Jakob

2017-01-01

To develop a population based statistical model of the systematic interfraction geometric variations between the planning CT and first treatment week of lung cancer patients for inclusion as uncertainty term in future probabilistic planning. Deformable image registrations between the planning CT and

A Total Variation Model Based on the Strictly Convex Modification for Image Denoising

Directory of Open Access Journals (Sweden)

Boying Wu

2014-01-01

Full Text Available We propose a strictly convex functional in which the regular term consists of the total variation term and an adaptive logarithm based convex modification term. We prove the existence and uniqueness of the minimizer for the proposed variational problem. The existence, uniqueness, and long-time behavior of the solution of the associated evolution system is also established. Finally, we present experimental results to illustrate the effectiveness of the model in noise reduction, and a comparison is made in relation to the more classical methods of the traditional total variation (TV, the Perona-Malik (PM, and the more recent D-α-PM method. Additional distinction from the other methods is that the parameters, for manual manipulation, in the proposed algorithm are reduced to basically only one.

Statistical model for OCT image denoising

KAUST Repository

Li, Muxingzi

2017-08-01

Optical coherence tomography (OCT) is a non-invasive technique with a large array of applications in clinical imaging and biological tissue visualization. However, the presence of speckle noise affects the analysis of OCT images and their diagnostic utility. In this article, we introduce a new OCT denoising algorithm. The proposed method is founded on a numerical optimization framework based on maximum-a-posteriori estimate of the noise-free OCT image. It combines a novel speckle noise model, derived from local statistics of empirical spectral domain OCT (SD-OCT) data, with a Huber variant of total variation regularization for edge preservation. The proposed approach exhibits satisfying results in terms of speckle noise reduction as well as edge preservation, at reduced computational cost.

Variations in the size of focal nodular hyperplasia on magnetic resonance imaging.

Science.gov (United States)

Ramírez-Fuentes, C; Martí-Bonmatí, L; Torregrosa, A; Del Val, A; Martínez, C

2013-01-01

To evaluate the changes in the size of focal nodular hyperplasia (FNH) during long-term magnetic resonance imaging (MRI) follow-up. We reviewed 44 FNHs in 30 patients studied with MRI with at least two MRI studies at least 12 months apart. We measured the largest diameter of the lesion (inmm) in contrast-enhanced axial images and calculated the percentage of variation as the difference between the maximum diameter in the follow-up and the maximum diameter in the initial study. We defined significant variation in size as variation greater than 20%. We also analyzed predisposing hormonal factors. The mean interval between the two imaging studies was 35±2 months (range: 12-94). Most lesions (80%) remained stable during follow-up. Only 9 of the 44 lesions (20%) showed a significant variation in diameter: 7 (16%) decreased in size and 2 (4%) increased, with variations that reached the double of the initial size. The change in size was not related to pregnancy, menopause, or the use of birth control pills or corticoids. Changes in the size of FNHs during follow-up are relatively common and should not lead to a change in the diagnosis. These variations in size seem to be independent of hormonal factors that are considered to predispose. Copyright © 2011 SERAM. Published by Elsevier Espana. All rights reserved.

Omics approaches to individual variation: modeling networks and the virtual patient

OpenAIRE

Lehrach, Hans

2016-01-01

Every human is unique. We differ in our genomes, environment, behavior, disease history, and past and current medical treatment?a complex catalog of differences that often leads to variations in the way each of us responds to a particular therapy. We argue here that true personalization of drug therapies will rely on ?virtual patient? models based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. The models will be based, wherever possible,...

Comparison of computerized digital and film-screen radiography: response to variation in imaging kVp

Energy Technology Data Exchange (ETDEWEB)

Broderick, N J; Long, B; Dreesen, R G; Cohen, M D; Cory, D A [Riley Hospital for Children, Indiana Univ. School of Medicine, Indianapolis, IN (United States). Dept. of Radiology; Katz, B P; Kalasinski, L A [Regenstreif Inst., Indiana Univ. School of Medicine, Indianapolis, IN (United States). Dept. of Medicine

1992-09-01

A controlled prospective study, in an animal model chosen to simulate portable neonatal radiography, was performed to compare the response of the Philips Computed Radiography (CR) system and conventional 200 speed film-screen (FS) to variation in imaging kVp. Acceptable images were obtained on the CR system over a very wide kVp range. In contrast the FS system produced acceptable images over a narrow kVp range. This ability suggests that the CR system should eliminate the need for repeat examinations in cases where a suboptimal kVp setting would have resulted in an unacceptable FS image. CR technology should therefore be ideally suited to portable radiography especially in situations where selection of correct exposure factors is difficult as in the neonatal nursery. (orig.).

Comparison of computerized digital and film-screen radiography: response to variation in imaging kVp

International Nuclear Information System (INIS)

Broderick, N.J.; Long, B.; Dreesen, R.G.; Cohen, M.D.; Cory, D.A.; Katz, B.P.; Kalasinski, L.A.

1992-01-01

A controlled prospective study, in an animal model chosen to simulate portable neonatal radiography, was performed to compare the response of the Philips Computed Radiography (CR) system and conventional 200 speed film-screen (FS) to variation in imaging kVp. Acceptable images were obtained on the CR system over a very wide kVp range. In contrast the FS system produced acceptable images over a narrow kVp range. This ability suggests that the CR system should eliminate the need for repeat examinations in cases where a suboptimal kVp setting would have resulted in an unacceptable FS image. CR technology should therefore be ideally suited to portable radiography especially in situations where selection of correct exposure factors is difficult as in the neonatal nursery. (orig.)

Automatic Fontanel Extraction from Newborns' CT Images Using Variational Level Set

Science.gov (United States)

Kazemi, Kamran; Ghadimi, Sona; Lyaghat, Alireza; Tarighati, Alla; Golshaeyan, Narjes; Abrishami-Moghaddam, Hamid; Grebe, Reinhard; Gondary-Jouet, Catherine; Wallois, Fabrice

A realistic head model is needed for source localization methods used for the study of epilepsy in neonates applying Electroencephalographic (EEG) measurements from the scalp. The earliest models consider the head as a series of concentric spheres, each layer corresponding to a different tissue whose conductivity is assumed to be homogeneous. The results of the source reconstruction depend highly on the electric conductivities of the tissues forming the head.The most used model is constituted of three layers (scalp, skull, and intracranial). Most of the major bones of the neonates’ skull are ossified at birth but can slightly move relative to each other. This is due to the sutures, fibrous membranes that at this stage of development connect the already ossified flat bones of the neurocranium. These weak parts of the neurocranium are called fontanels. Thus it is important to enter the exact geometry of fontaneles and flat bone in a source reconstruction because they show pronounced in conductivity. Computer Tomography (CT) imaging provides an excellent tool for non-invasive investigation of the skull which expresses itself in high contrast to all other tissues while the fontanels only can be identified as absence of bone, gaps in the skull formed by flat bone. Therefore, the aim of this paper is to extract the fontanels from CT images applying a variational level set method. We applied the proposed method to CT-images of five different subjects. The automatically extracted fontanels show good agreement with the manually extracted ones.

An Improved Variational Method for Hyperspectral Image Pansharpening with the Constraint of Spectral Difference Minimization

Science.gov (United States)

Huang, Z.; Chen, Q.; Shen, Y.; Chen, Q.; Liu, X.

2017-09-01

Variational pansharpening can enhance the spatial resolution of a hyperspectral (HS) image using a high-resolution panchromatic (PAN) image. However, this technology may lead to spectral distortion that obviously affect the accuracy of data analysis. In this article, we propose an improved variational method for HS image pansharpening with the constraint of spectral difference minimization. We extend the energy function of the classic variational pansharpening method by adding a new spectral fidelity term. This fidelity term is designed following the definition of spectral angle mapper, which means that for every pixel, the spectral difference value of any two bands in the HS image is in equal proportion to that of the two corresponding bands in the pansharpened image. Gradient descent method is adopted to find the optimal solution of the modified energy function, and the pansharpened image can be reconstructed. Experimental results demonstrate that the constraint of spectral difference minimization is able to preserve the original spectral information well in HS images, and reduce the spectral distortion effectively. Compared to original variational method, our method performs better in both visual and quantitative evaluation, and achieves a good trade-off between spatial and spectral information.

Variational contrast enhancement guided by global and local contrast measurements for single-image defogging

Science.gov (United States)

Zhou, Li; Bi, Du-Yan; He, Lin-Yuan

2015-01-01

The visibility of images captured in foggy conditions is impaired severely by a decrease in the contrasts of objects and veiling with a characteristic gray hue, which may limit the performance of visual applications out of doors. Contrast enhancement together with color restoration is a challenging mission for conventional fog-removal methods, as the degrading effect of fog is largely dependent on scene depth information. Nowadays, people change their minds by establishing a variational framework for contrast enhancement based on a physically based analytical model, unexpectedly resulting in color distortion, dark-patch distortion, or fuzzy features of local regions. Unlike previous work, our method treats an atmospheric veil as a scattering disturbance and formulates a foggy image as an energy functional minimization to estimate direct attenuation, originating from the work of image denoising. In addition to a global contrast measurement based on a total variation norm, an additional local measurement is designed in that optimal problem for the purpose of digging out more local details as well as suppressing dark-patch distortion. Moreover, we estimate the airlight precisely by maximization with a geometric constraint and a natural image prior in order to protect the faithfulness of the scene color. With the estimated direct attenuation and airlight, the fog-free image can be restored. Finally, our method is tested on several benchmark and realistic images evaluated by two assessment approaches. The experimental results imply that our proposed method works well compared with the state-of-the-art defogging methods.

Variation in the human ribs geometrical properties and mechanical response based on X-ray computed tomography images resolution.

Science.gov (United States)

Perz, Rafał; Toczyski, Jacek; Subit, Damien

2015-01-01

Computational models of the human body are commonly used for injury prediction in automobile safety research. To create these models, the geometry of the human body is typically obtained from segmentation of medical images such as computed tomography (CT) images that have a resolution between 0.2 and 1mm/pixel. While the accuracy of the geometrical and structural information obtained from these images depend greatly on their resolution, the effect of image resolution on the estimation of the ribs geometrical properties has yet to be established. To do so, each of the thirty-four sections of ribs obtained from a Post Mortem Human Surrogate (PMHS) was imaged using three different CT modalities: standard clinical CT (clinCT), high resolution clinical CT (HRclinCT), and microCT. The images were processed to estimate the rib cross-section geometry and mechanical properties, and the results were compared to those obtained from the microCT images by computing the 'deviation factor', a metric that quantifies the relative difference between results obtained from clinCT and HRclinCT to those obtained from microCT. Overall, clinCT images gave a deviation greater than 100%, and were therefore deemed inadequate for the purpose of this study. HRclinCT overestimated the rib cross-sectional area by 7.6%, the moments of inertia by about 50%, and the cortical shell area by 40.2%, while underestimating the trabecular area by 14.7%. Next, a parametric analysis was performed to quantify how the variations in the estimate of the geometrical properties affected the rib predicted mechanical response under antero-posterior loading. A variation of up to 45% for the predicted peak force and up to 50% for the predicted stiffness was observed. These results provide a quantitative estimate of the sensitivity of the response of the FE model to the resolution of the images used to generate it. They also suggest that a correction factor could be derived from the comparison between microCT and

Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

Directory of Open Access Journals (Sweden)

Hongyao Deng

2017-01-01

Full Text Available Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness.

The numerical solution of total variation minimization problems in image processing

Energy Technology Data Exchange (ETDEWEB)

Vogel, C.R.; Oman, M.E. [Montana State Univ., Bozeman, MT (United States)

1994-12-31

Consider the minimization of penalized least squares functionals of the form: f(u) = 1/2 ({parallel}Au {minus} z{parallel}){sup 2} + {alpha}{integral}{sub {Omega}}{vert_bar}{del}u{vert_bar}dx. Here A is a bounded linear operator, z represents data, {parallel} {center_dot} {parallel} is a Hilbert space norm, {alpha} is a positive parameter, {integral}{sub {Omega}}{vert_bar}{del}u{vert_bar} dx represents the total variation (TV) of a function u {element_of} BV ({Omega}), the class of functions of bounded variation on a bounded region {Omega}, and {vert_bar} {center_dot} {vert_bar} denotes Euclidean norm. In image processing, u represents an image which is to be recovered from noisy data z. Certain {open_quotes}blurring processes{close_quotes} may be represented by the action of an operator A on the image u.

Total Variation Based Parameter-Free Model for Impulse Noise Removal

DEFF Research Database (Denmark)

Sciacchitano, Federica; Dong, Yiqiu; Andersen, Martin Skovgaard

2017-01-01

We propose a new two-phase method for reconstruction of blurred images corrupted by impulse noise. In the first phase, we use a noise detector to identify the pixels that are contaminated by noise, and then, in the second phase, we reconstruct the noisy pixels by solving an equality constrained...... total variation minimization problem that preserves the exact values of the noise-free pixels. For images that are only corrupted by impulse noise (i. e., not blurred) we apply the semismooth Newton's method to a reduced problem, and if the images are also blurred, we solve the equality constrained...... reconstruction problem using a first-order primal-dual algorithm. The proposed model improves the computational efficiency (in the denoising case) and has the advantage of being regularization parameter-free. Our numerical results suggest that the method is competitive in terms of its restoration capabilities...

Color correction with blind image restoration based on multiple images using a low-rank model

Science.gov (United States)

Li, Dong; Xie, Xudong; Lam, Kin-Man

2014-03-01

We present a method that can handle the color correction of multiple photographs with blind image restoration simultaneously and automatically. We prove that the local colors of a set of images of the same scene exhibit the low-rank property locally both before and after a color-correction operation. This property allows us to correct all kinds of errors in an image under a low-rank matrix model without particular priors or assumptions. The possible errors may be caused by changes of viewpoint, large illumination variations, gross pixel corruptions, partial occlusions, etc. Furthermore, a new iterative soft-segmentation method is proposed for local color transfer using color influence maps. Due to the fact that the correct color information and the spatial information of images can be recovered using the low-rank model, more precise color correction and many other image-restoration tasks-including image denoising, image deblurring, and gray-scale image colorizing-can be performed simultaneously. Experiments have verified that our method can achieve consistent and promising results on uncontrolled real photographs acquired from the Internet and that it outperforms current state-of-the-art methods.

BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS

Directory of Open Access Journals (Sweden)

J. Mu

2017-05-01

Full Text Available In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

Robust bladder image registration by redefining data-term in total variational approach

Science.gov (United States)

Ali, Sharib; Daul, Christian; Galbrun, Ernest; Amouroux, Marine; Guillemin, François; Blondel, Walter

2015-03-01

Cystoscopy is the standard procedure for clinical diagnosis of bladder cancer diagnosis. Bladder carcinoma in situ are often multifocal and spread over large areas. In vivo, localization and follow-up of these tumors and their nearby sites is necessary. But, due to the small field of view (FOV) of the cystoscopic video images, urologists cannot easily interpret the scene. Bladder mosaicing using image registration facilitates this interpretation through the visualization of entire lesions with respect to anatomical landmarks. The reference white light (WL) modality is affected by a strong variability in terms of texture, illumination conditions and motion blur. Moreover, in the complementary fluorescence light (FL) modality, the texture is visually different from that of the WL. Existing algorithms were developed for a particular modality and scene conditions. This paper proposes a more general on fly image registration approach for dealing with these variability issues in cystoscopy. To do so, we present a novel, robust and accurate image registration scheme by redefining the data-term of the classical total variational (TV) approach. Quantitative results on realistic bladder phantom images are used for verifying accuracy and robustness of the proposed model. This method is also qualitatively assessed with patient data mosaicing for both WL and FL modalities.

Chambolle's Projection Algorithm for Total Variation Denoising

Directory of Open Access Journals (Sweden)

Joan Duran

2013-12-01

Full Text Available Denoising is the problem of removing the inherent noise from an image. The standard noise model is additive white Gaussian noise, where the observed image f is related to the underlying true image u by the degradation model f=u+n, and n is supposed to be at each pixel independently and identically distributed as a zero-mean Gaussian random variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi introduced the total variation as a regularizing term. It has proved to be quite efficient for regularizing images without smoothing the boundaries of the objects. This paper focuses on the simple description of the theory and on the implementation of Chambolle's projection algorithm for minimizing the total variation of a grayscale image. Furthermore, we adapt the algorithm to the vectorial total variation for color images. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.

Gamma-variate modeling of indicator dilution curves in electrical impedance tomography.

Science.gov (United States)

Hentze, Benjamin; Muders, Thomas; Luepschen, Henning; Leonhardt, Steffen; Putensen, Christian; Walter, Marian

2017-07-01

Electrical impedance tomography (EIT) is a non-invasive imaging technique, that can be used to monitor regional lung ventilation (V̇) in intensive care units (ICU) at bedside. This work introduces a method to extract regional lung perfusion (Q̇) from EIT image streams in order to quantify regional gas exchange in the lungs. EIT data from a single porcine animal trial, recorded during injection of a contrast agent (NaCl 10%) into a central venous catheter (CVC), are used for evaluation. Using semi-negative matrix factorization (Semi-NMF) a set of source signals is extracted from the data. A subsequent non-linear fit of a gamma-variate model to the source signals results in model signals, describing contrast agent flow through the cardio-pulmonary system. A linear fit of the model signals to the EIT image stream then yields functional images ofQ̇. Additionally, a pulmonary transit function (PTF) and parameters, such as mean transit time (MTT), time to peak (TTP) and area under curve (AUC) are derived. In result, EIT was used to track changes of regional lung ventilation to perfusion ratio (V̇/Q̇) during changes of positive end-expiratory pressure (PEEP). Furthermore, correlations of MTT and AUC with cardiac output (CO) indicate that CO measurement by EIT might be possible.

Skin image illumination modeling and chromophore identification for melanoma diagnosis

Science.gov (United States)

Liu, Zhao; Zerubia, Josiane

2015-05-01

The presence of illumination variation in dermatological images has a negative impact on the automatic detection and analysis of cutaneous lesions. This paper proposes a new illumination modeling and chromophore identification method to correct lighting variation in skin lesion images, as well as to extract melanin and hemoglobin concentrations of human skin, based on an adaptive bilateral decomposition and a weighted polynomial curve fitting, with the knowledge of a multi-layered skin model. Different from state-of-the-art approaches based on the Lambert law, the proposed method, considering both specular reflection and diffuse reflection of the skin, enables us to address highlight and strong shading effects usually existing in skin color images captured in an uncontrolled environment. The derived melanin and hemoglobin indices, directly relating to the pathological tissue conditions, tend to be less influenced by external imaging factors and are more efficient in describing pigmentation distributions. Experiments show that the proposed method gave better visual results and superior lesion segmentation, when compared to two other illumination correction algorithms, both designed specifically for dermatological images. For computer-aided diagnosis of melanoma, sensitivity achieves 85.52% when using our chromophore descriptors, which is 8~20% higher than those derived from other color descriptors. This demonstrates the benefit of the proposed method for automatic skin disease analysis.

Variational methods in molecular modeling

CERN Document Server

2017-01-01

This book presents tutorial overviews for many applications of variational methods to molecular modeling. Topics discussed include the Gibbs-Bogoliubov-Feynman variational principle, square-gradient models, classical density functional theories, self-consistent-field theories, phase-field methods, Ginzburg-Landau and Helfrich-type phenomenological models, dynamical density functional theory, and variational Monte Carlo methods. Illustrative examples are given to facilitate understanding of the basic concepts and quantitative prediction of the properties and rich behavior of diverse many-body systems ranging from inhomogeneous fluids, electrolytes and ionic liquids in micropores, colloidal dispersions, liquid crystals, polymer blends, lipid membranes, microemulsions, magnetic materials and high-temperature superconductors. All chapters are written by leading experts in the field and illustrated with tutorial examples for their practical applications to specific subjects. With emphasis placed on physical unders...

Effects of small variations of speed of sound in optoacoustic tomographic imaging

International Nuclear Information System (INIS)

Deán-Ben, X. Luís; Ntziachristos, Vasilis; Razansky, Daniel

2014-01-01

Purpose: Speed of sound difference in the imaged object and surrounding coupling medium may reduce the resolution and overall quality of optoacoustic tomographic reconstructions obtained by assuming a uniform acoustic medium. In this work, the authors investigate the effects of acoustic heterogeneities and discuss potential benefits of accounting for those during the reconstruction procedure. Methods: The time shift of optoacoustic signals in an acoustically heterogeneous medium is studied theoretically by comparing different continuous and discrete wave propagation models. A modification of filtered back-projection reconstruction is subsequently implemented by considering a straight acoustic rays model for ultrasound propagation. The results obtained with this reconstruction procedure are compared numerically and experimentally to those obtained assuming a heuristically fitted uniform speed of sound in both full-view and limited-view optoacoustic tomography scenarios. Results: The theoretical analysis showcases that the errors in the time-of-flight of the signals predicted by considering the straight acoustic rays model tend to be generally small. When using this model for reconstructing simulated data, the resulting images accurately represent the theoretical ones. On the other hand, significant deviations in the location of the absorbing structures are found when using a uniform speed of sound assumption. The experimental results obtained with tissue-mimicking phantoms and a mouse postmortem are found to be consistent with the numerical simulations. Conclusions: Accurate analysis of effects of small speed of sound variations demonstrates that accounting for differences in the speed of sound allows improving optoacoustic reconstruction results in realistic imaging scenarios involving acoustic heterogeneities in tissues and surrounding media

Robust Image Regression Based on the Extended Matrix Variate Power Exponential Distribution of Dependent Noise.

Science.gov (United States)

Luo, Lei; Yang, Jian; Qian, Jianjun; Tai, Ying; Lu, Gui-Fu

2017-09-01

Dealing with partial occlusion or illumination is one of the most challenging problems in image representation and classification. In this problem, the characterization of the representation error plays a crucial role. In most current approaches, the error matrix needs to be stretched into a vector and each element is assumed to be independently corrupted. This ignores the dependence between the elements of error. In this paper, it is assumed that the error image caused by partial occlusion or illumination changes is a random matrix variate and follows the extended matrix variate power exponential distribution. This has the heavy tailed regions and can be used to describe a matrix pattern of l×m dimensional observations that are not independent. This paper reveals the essence of the proposed distribution: it actually alleviates the correlations between pixels in an error matrix E and makes E approximately Gaussian. On the basis of this distribution, we derive a Schatten p -norm-based matrix regression model with L q regularization. Alternating direction method of multipliers is applied to solve this model. To get a closed-form solution in each step of the algorithm, two singular value function thresholding operators are introduced. In addition, the extended Schatten p -norm is utilized to characterize the distance between the test samples and classes in the design of the classifier. Extensive experimental results for image reconstruction and classification with structural noise demonstrate that the proposed algorithm works much more robustly than some existing regression-based methods.

Human cerebral cortices: signal variation on diffusion-weighted MR imaging

Energy Technology Data Exchange (ETDEWEB)

Asao, Chiaki [Kumamoto Regional Medical Center, Department of Radiology, Kumamoto (Japan); National Hospital Organization Kumamoto Medical Center, Department of Radiology, Kumamoto (Japan); Hirai, Toshinori; Yamashita, Yasuyuki [Kumamoto University Graduate School of Medical Sciences, Department of Diagnostic Radiology, Kumamoto (Japan); Yoshimatsu, Shunji [National Hospital Organization Kumamoto Medical Center, Department of Radiology, Kumamoto (Japan); Matsukawa, Tetsuya; Imuta, Masanori [Kumamoto Regional Medical Center, Department of Radiology, Kumamoto (Japan); Sagara, Katsuro [Kumamoto Regional Medical Center, Department of Internal Medicine, Kumamoto (Japan)

2008-03-15

We have often encountered high signal intensity (SI) of the cingulate gyrus and insula during diffusion-weighted magnetic resonance imaging (DW-MRI) on neurologically healthy adults. To date, cortical signal heterogeneity on DW images has not been investigated systematically. The purpose of our study was to determine whether there is regional signal variation in the brain cortices of neurologically healthy adults on DW-MR images. The SI of the cerebral cortices on DW-MR images at 1.5 T was evaluated in 50 neurologically healthy subjects (34 men, 16 women; age range 33-84 years; mean age 57.6 years). The cortical SI in the cingulate gyrus, insula, and temporal, occipital, and parietal lobes was graded relative to the SI of the frontal lobe. Contrast-to-noise ratios (CNRs) on DW-MR images were compared for each cortical area. Diffusion changes were analyzed by visually assessment of the differences in appearance among the cortices on apparent diffusion coefficient (ADC) maps. Increased SI was frequently seen in the cingulate gyrus and insula regardless of patient age. There were no significant gender- or laterality-related differences. The CNR was significantly higher in the cingulate gyrus and insula than in the other cortices (p <.01), and significant differences existed among the cortical regions (p <.001). There were no apparent ADC differences among the cortices on ADC maps. Regional signal variation of the brain cortices was observed on DW-MR images of healthy subjects, and the cingulate gyrus and insula frequently manifested high SI. These findings may help in the recognition of cortical signal abnormalities as visualized on DW-MR images. (orig.)

Human cerebral cortices: signal variation on diffusion-weighted MR imaging

International Nuclear Information System (INIS)

Asao, Chiaki; Hirai, Toshinori; Yamashita, Yasuyuki; Yoshimatsu, Shunji; Matsukawa, Tetsuya; Imuta, Masanori; Sagara, Katsuro

2008-01-01

We have often encountered high signal intensity (SI) of the cingulate gyrus and insula during diffusion-weighted magnetic resonance imaging (DW-MRI) on neurologically healthy adults. To date, cortical signal heterogeneity on DW images has not been investigated systematically. The purpose of our study was to determine whether there is regional signal variation in the brain cortices of neurologically healthy adults on DW-MR images. The SI of the cerebral cortices on DW-MR images at 1.5 T was evaluated in 50 neurologically healthy subjects (34 men, 16 women; age range 33-84 years; mean age 57.6 years). The cortical SI in the cingulate gyrus, insula, and temporal, occipital, and parietal lobes was graded relative to the SI of the frontal lobe. Contrast-to-noise ratios (CNRs) on DW-MR images were compared for each cortical area. Diffusion changes were analyzed by visually assessment of the differences in appearance among the cortices on apparent diffusion coefficient (ADC) maps. Increased SI was frequently seen in the cingulate gyrus and insula regardless of patient age. There were no significant gender- or laterality-related differences. The CNR was significantly higher in the cingulate gyrus and insula than in the other cortices (p <.01), and significant differences existed among the cortical regions (p <.001). There were no apparent ADC differences among the cortices on ADC maps. Regional signal variation of the brain cortices was observed on DW-MR images of healthy subjects, and the cingulate gyrus and insula frequently manifested high SI. These findings may help in the recognition of cortical signal abnormalities as visualized on DW-MR images. (orig.)

A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Image Segmentation

Science.gov (United States)

2016-05-01

norm does not cap - ture the geometry completely. The L1−L2 in (c) does a better job than TV while L1 in (b) and L1−0.5L2 in (d) capture the squares most...and isotropic total variation (TV) norms into a relaxed formu- lation of the two phase Mumford-Shah (MS) model for image segmentation. We show...results exceeding those obtained by the MS model when using the standard TV norm to regular- ize partition boundaries. In particular, examples illustrating

Beam-hardening correction in CT based on basis image and TV model

International Nuclear Information System (INIS)

Li Qingliang; Yan Bin; Li Lei; Sun Hongsheng; Zhang Feng

2012-01-01

In X-ray computed tomography, the beam hardening leads to artifacts and reduces the image quality. It analyzes how beam hardening influences on original projection. According, it puts forward a kind of new beam-hardening correction method based on the basis images and TV model. Firstly, according to physical characteristics of the beam hardening an preliminary correction model with adjustable parameters is set up. Secondly, using different parameters, original projections are operated by the correction model. Thirdly, the projections are reconstructed to obtain a series of basis images. Finally, the linear combination of basis images is the final reconstruction image. Here, with total variation for the final reconstruction image as the cost function, the linear combination coefficients for the basis images are determined according to iterative method. To verify the effectiveness of the proposed method, the experiments are carried out on real phantom and industrial part. The results show that the algorithm significantly inhibits cup and strip artifacts in CT image. (authors)

Regularization by Functions of Bounded Variation and Applications to Image Enhancement

International Nuclear Information System (INIS)

Casas, E.; Kunisch, K.; Pola, C.

1999-01-01

Optimization problems regularized by bounded variation seminorms are analyzed. The optimality system is obtained and finite-dimensional approximations of bounded variation function spaces as well as of the optimization problems are studied. It is demonstrated that the choice of the vector norm in the definition of the bounded variation seminorm is of special importance for approximating subspaces consisting of piecewise constant functions. Algorithms based on a primal-dual framework that exploit the structure of these nondifferentiable optimization problems are proposed. Numerical examples are given for denoising of blocky images with very high noise

Mixed Gaussian-Impulse Noise Image Restoration Via Total Variation

Science.gov (United States)

2012-05-01

deblurring under impulse noise ,” J. Math. Imaging Vis., vol. 36, pp. 46–53, January 2010. [5] B. Li, Q. Liu, J. Xu, and X. Luo, “A new method for removing......Several Total Variation (TV) regularization methods have recently been proposed to address denoising under mixed Gaussian and impulse noise . While

The Research of Optical Turbulence Model in Underwater Imaging System

Directory of Open Access Journals (Sweden)

Liying Sun

2014-01-01

Full Text Available In order to research the effect of turbulence on underwater imaging system and image restoration, the underwater turbulence model is simulated by computer fluid dynamics. This model is obtained in different underwater turbulence intensity, which contains the pressure data that influences refractive index distribution. When the pressure value is conversed to refractive index, the refractive index distribution can be received with the refraction formula. In the condition of same turbulent intensity, the distribution of refractive index presents gradient in the whole region, with disorder and mutations in the local region. With the turbulence intensity increase, the holistic variation of the refractive index in the image is larger, and the refractive index change more tempestuously in the local region. All the above are illustrated by the simulation results with he ray tracing method and turbulent refractive index model. According to different turbulence intensity analysis, it is proved that turbulence causes image distortion and increases noise.

On Feature Relevance in Image-Based Prediction Models: An Empirical Study

DEFF Research Database (Denmark)

Konukoglu, E.; Ganz, Melanie; Van Leemput, Koen

2013-01-01

Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the co...

Monte Carlo modelling of a-Si EPID response: The effect of spectral variations with field size and position

International Nuclear Information System (INIS)

Parent, Laure; Seco, Joao; Evans, Phil M.; Fielding, Andrew; Dance, David R.

2006-01-01

This study focused on predicting the electronic portal imaging device (EPID) image of intensity modulated radiation treatment (IMRT) fields in the absence of attenuation material in the beam with Monte Carlo methods. As IMRT treatments consist of a series of segments of various sizes that are not always delivered on the central axis, large spectral variations may be observed between the segments. The effect of these spectral variations on the EPID response was studied with fields of various sizes and off-axis positions. A detailed description of the EPID was implemented in a Monte Carlo model. The EPID model was validated by comparing the EPID output factors for field sizes between 1x1 and 26x26 cm 2 at the isocenter. The Monte Carlo simulations agreed with the measurements to within 1.5%. The Monte Carlo model succeeded in predicting the EPID response at the center of the fields of various sizes and offsets to within 1% of the measurements. Large variations (up to 29%) of the EPID response were observed between the various offsets. The EPID response increased with field size and with field offset for most cases. The Monte Carlo model was then used to predict the image of a simple test IMRT field delivered on the beam axis and with an offset. A variation of EPID response up to 28% was found between the on- and off-axis delivery. Finally, two clinical IMRT fields were simulated and compared to the measurements. For all IMRT fields, simulations and measurements agreed within 3%--0.2 cm for 98% of the pixels. The spectral variations were quantified by extracting from the spectra at the center of the fields the total photon yield (Y total ), the photon yield below 1 MeV (Y low ), and the percentage of photons below 1 MeV (P low ). For the studied cases, a correlation was shown between the EPID response variation and Y total , Y low , and P low

Continuous monitoring of arthritis in animal models using optical imaging modalities

Science.gov (United States)

Son, Taeyoon; Yoon, Hyung-Ju; Lee, Saseong; Jang, Won Seuk; Jung, Byungjo; Kim, Wan-Uk

2014-10-01

Given the several difficulties associated with histology, including difficulty in continuous monitoring, this study aimed to investigate the feasibility of optical imaging modalities-cross-polarization color (CPC) imaging, erythema index (EI) imaging, and laser speckle contrast (LSC) imaging-for continuous evaluation and monitoring of arthritis in animal models. C57BL/6 mice, used for the evaluation of arthritis, were divided into three groups: arthritic mice group (AMG), positive control mice group (PCMG), and negative control mice group (NCMG). Complete Freund's adjuvant, mineral oil, and saline were injected into the footpad for AMG, PCMG, and NCMG, respectively. LSC and CPC images were acquired from 0 through 144 h after injection for all groups. EI images were calculated from CPC images. Variations in feet area, EI, and speckle index for each mice group over time were calculated for quantitative evaluation of arthritis. Histological examinations were performed, and the results were found to be consistent with those from optical imaging analysis. Thus, optical imaging modalities may be successfully applied for continuous evaluation and monitoring of arthritis in animal models.

High-resolution magnetic resonance imaging of diurnal variations in rheumatoid arthritis

International Nuclear Information System (INIS)

Nicholas, R.S.

2000-09-01

This thesis describes work that uses high-resolution magnetic resonance imaging (MRI) to give an insight into the aetiology of rheumatoid arthritis (RA) with particular reference to characterising diurnal changes in joint stiffness in the metacarpophalangeal (MCP) joints. The study was performed on a targeted 1.1 T MRI scanner using specialised sequences, including 3-dimensional gradient-echo, magnetisation transfer (MT) and multiple gradient-echo. These enabled tissue-dependent parameters such as MT ratio, effective transverse relaxation time (T 2 *) and proton density (ρ) to be made. Preliminary reproducibility studies of the MRI measurements showed that MT ratio could be measured in vivo to an accuracy of better than 8%. This variation is due to repositioning errors and physiological changes. Equivalent variations in T 2 * and p were 23% and 16% respectively; these poorer figures were contributed to errors in fitting the data to an exponential curve. An MRI study monitoring the diurnal variation of stiffness in RA demonstrated better characterisation of the disease state using MT and T 2 * maps compared to standard gradient-echo imaging. Features associated with arthritis such as bone erosions and cysts were found in the control group and an MT age dependence was measured in the soft tissue on the superior margin of the joint. This region also exhibited a diurnal variation in MT ratio for the patient group. The interaction between this region of tissue and other structures (e.g. the sheath of extensor tendon) within the joint could be a possible cause of joint stiffness. An incidental finding of this study was that Ritchie joint score also showed a diurnal variation. This study has demonstrated that MRI can be used to make reproducible measurements of the diurnal variations in RA. The indication is that the soft tissues in the superior aspect of the joint may be responsible for the symptom of joint stiffness in the MCP joints and future studies should be

Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets

KAUST Repository

Lenzen, F.; Lellmann, J.; Becker, F.; Schnö rr, C.

2014-01-01

© 2014 Society for Industrial and Applied Mathematics. We consider a class of quasi-variational inequalities (QVIs) for adaptive image restoration, where the adaptivity is described via solution-dependent constraint sets. In previous work we studied

An interior-point method for total variation regularized positron emission tomography image reconstruction

Science.gov (United States)

Bai, Bing

2012-03-01

There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.

Photometric Modeling of Simulated Surace-Resolved Bennu Images

Science.gov (United States)

Golish, D.; DellaGiustina, D. N.; Clark, B.; Li, J. Y.; Zou, X. D.; Bennett, C. A.; Lauretta, D. S.

2017-12-01

The Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) is a NASA mission to study and return a sample of asteroid (101955) Bennu. Imaging data from the mission will be used to develop empirical surface-resolved photometric models of Bennu at a series of wavelengths. These models will be used to photometrically correct panchromatic and color base maps of Bennu, compensating for variations due to shadows and photometric angle differences, thereby minimizing seams in mosaicked images. Well-corrected mosaics are critical to the generation of a global hazard map and a global 1064-nm reflectance map which predicts LIDAR response. These data products directly feed into the selection of a site from which to safely acquire a sample. We also require photometric correction for the creation of color ratio maps of Bennu. Color ratios maps provide insight into the composition and geological history of the surface and allow for comparison to other Solar System small bodies. In advance of OSIRIS-REx's arrival at Bennu, we use simulated images to judge the efficacy of both the photometric modeling software and the mission observation plan. Our simulation software is based on USGS's Integrated Software for Imagers and Spectrometers (ISIS) and uses a synthetic shape model, a camera model, and an empirical photometric model to generate simulated images. This approach gives us the flexibility to create simulated images of Bennu based on analog surfaces from other small Solar System bodies and to test our modeling software under those conditions. Our photometric modeling software fits image data to several conventional empirical photometric models and produces the best fit model parameters. The process is largely automated, which is crucial to the efficient production of data products during proximity operations. The software also produces several metrics on the quality of the observations themselves, such as surface coverage and the

Image-Based Models for Specularity Propagation in Diminished Reality.

Science.gov (United States)

Said, Souheil Hadj; Tamaazousti, Mohamed; Bartoli, Adrien

2018-07-01

The aim of Diminished Reality (DR) is to remove a target object in a live video stream seamlessly. In our approach, the area of the target object is replaced with new texture that blends with the rest of the image. The result is then propagated to the next frames of the video. One of the important stages of this technique is to update the target region with respect to the illumination change. This is a complex and recurrent problem when the viewpoint changes. We show that the state-of-the-art in DR fails in solving this problem, even under simple scenarios. We then use local illumination models to address this problem. According to these models, the variation in illumination only affects the specular component of the image. In the context of DR, the problem is therefore solved by propagating the specularities in the target area. We list a set of structural properties of specularities which we incorporate in two new models for specularity propagation. Our first model includes the same property as the previous approaches, which is the smoothness of illumination variation, but has a different estimation method based on the Thin-Plate Spline. Our second model incorporates more properties of the specularity's shape on planar surfaces. Experimental results on synthetic and real data show that our strategy substantially improves the rendering quality compared to the state-of-the-art in DR.

Intra- and interspecific variation in tropical tree and liana phenology derived from Unmanned Aerial Vehicle images

Science.gov (United States)

Bohlman, S.; Park, J.; Muller-Landau, H. C.; Rifai, S. W.; Dandois, J. P.

2017-12-01

Phenology is a critical driver of ecosystem processes. There is strong evidence that phenology is shifting in temperate ecosystems in response to climate change, but tropical tree and liana phenology remains poorly quantified and understood. A key challenge is that tropical forests contain hundreds of plant species with a wide variety of phenological patterns. Satellite-based observations, an important source of phenology data in northern latitudes, are hindered by frequent cloud cover in the tropics. To quantify phenology over a large number of individuals and species, we collected bi-weekly images from unmanned aerial vehicles (UAVs) in the well-studied 50-ha forest inventory plot on Barro Colorado Island, Panama. Between October 2014 and December 2015 and again in May 2015, we collected a total of 35 sets of UAV images, each with continuous coverage of the 50-ha plot, where every tree ≥ 1 cm DBH is mapped. Spectral, texture, and image information was extracted from the UAV images for individual tree crowns, which was then used as inputs for a machine learning algorithm to predict percent leaf and branch cover. We obtained the species identities of 2000 crowns in the images via field mapping. The objectives of this study are to (1) determined if machine learning algorithms, applied to UAV images, can effectively quantify changes in leaf cover, which we term "deciduousness; (2) determine how liana cover effects deciduousness and (3) test how well UAV-derived deciduousness patterns match satellite-derived temporal patterns. Machine learning algorithms trained on a variety of image parameters could effectively determine leaf cover, despite variation in lighting and viewing angles. Crowns with higher liana cover have less overall deciduousness (tree + liana together) than crowns with lower liana cover. Individual crown deciduousness, summed over all crowns measured in the 50-ha plot, showed a similar seasonal pattern as MODIS EVI composited over 10 years. However

Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages

Science.gov (United States)

Lahmiri, Salim; Shmuel, Amir

2017-11-01

Diabetic retinopathy is a disease that can cause a loss of vision. An early and accurate diagnosis helps to improve treatment of the disease and prognosis. One of the earliest characteristics of diabetic retinopathy is the appearance of retinal hemorrhages. The purpose of this study is to design a fully automated system for the detection of hemorrhages in a retinal image. In the first stage of our proposed system, a retinal image is processed with variational mode decomposition (VMD) to obtain the first variational mode, which captures the high frequency components of the original image. In the second stage, four texture descriptors are extracted from the first variational mode. Finally, a classifier trained with all computed texture descriptors is used to distinguish between images of healthy and unhealthy retinas with hemorrhages. Experimental results showed evidence of the effectiveness of the proposed system for detection of hemorrhages in the retina, since a perfect detection rate was achieved. Our proposed system for detecting diabetic retinopathy is simple and easy to implement. It requires only short processing time, and it yields higher accuracy in comparison with previously proposed methods for detecting diabetic retinopathy.

Radar correlated imaging for extended target by the combination of negative exponential restraint and total variation

Science.gov (United States)

Qian, Tingting; Wang, Lianlian; Lu, Guanghua

2017-07-01

Radar correlated imaging (RCI) introduces the optical correlated imaging technology to traditional microwave imaging, which has raised widespread concern recently. Conventional RCI methods neglect the structural information of complex extended target, which makes the quality of recovery result not really perfect, thus a novel combination of negative exponential restraint and total variation (NER-TV) algorithm for extended target imaging is proposed in this paper. The sparsity is measured by a sequential order one negative exponential function, then the 2D total variation technique is introduced to design a novel optimization problem for extended target imaging. And the proven alternating direction method of multipliers is applied to solve the new problem. Experimental results show that the proposed algorithm could realize high resolution imaging efficiently for extended target.

An Optimal DEM Reconstruction Method for Linear Array Synthetic Aperture Radar Based on Variational Model

Directory of Open Access Journals (Sweden)

Shi Jun

2015-02-01

Full Text Available Downward-looking Linear Array Synthetic Aperture Radar (LASAR has many potential applications in the topographic mapping, disaster monitoring and reconnaissance applications, especially in the mountainous area. However, limited by the sizes of platforms, its resolution in the linear array direction is always far lower than those in the range and azimuth directions. This disadvantage leads to the blurring of Three-Dimensional (3D images in the linear array direction, and restricts the application of LASAR. To date, the research on 3D SAR image enhancement has focused on the sparse recovery technique. In this case, the one-to-one mapping of Digital Elevation Model (DEM brakes down. To overcome this, an optimal DEM reconstruction method for LASAR based on the variational model is discussed in an effort to optimize the DEM and the associated scattering coefficient map, and to minimize the Mean Square Error (MSE. Using simulation experiments, it is found that the variational model is more suitable for DEM enhancement applications to all kinds of terrains compared with the Orthogonal Matching Pursuit (OMPand Least Absolute Shrinkage and Selection Operator (LASSO methods.

A generalized model for optimal transport of images including dissipation and density modulation

KAUST Repository

Maas, Jan; Rumpf, Martin; Schö nlieb, Carola; Simon, Stefan

2015-01-01

transport to strongly dissipative dynamics. For this model a robust and effective variational time discretization of geodesic paths is proposed. This requires to minimize a discrete path energy consisting of a sum of consecutive image matching functionals

A variational void coalescence model for ductile metals

KAUST Repository

Siddiq, Amir; Arciniega, Roman; El Sayed, Tamer

2011-01-01

We present a variational void coalescence model that includes all the essential ingredients of failure in ductile porous metals. The model is an extension of the variational void growth model by Weinberg et al. (Comput Mech 37:142-152, 2006

Modeling of Craniofacial Anatomy, Variation, and Growth

DEFF Research Database (Denmark)

Thorup, Signe Strann

The topic of this thesis is automatic analysis of craniofacial images with respect to changes due to growth and surgery, inter-subject variation and intracranial volume estimation. The methods proposed contribute to the knowledge about specific craniofacial anomalies, as well as provide a tool...... for detailed analyses for clinical and research purposes. Most of the applications in this thesis rely on non-rigid image registration by the means of warping one image into the coordinate system of another image. This warping results in a deformation field that describes the anatomical correspondence between......, thus creating a personalized atlas. The knowledge built into the atlas is e.g. location of anatomical regions and landmarks of importance to surgery planning and evaluation or population studies. With these correspondences, various analyses could be carried out e.g. quantification of growth, inter...

Accelerating cross-validation with total variation and its application to super-resolution imaging.

Directory of Open Access Journals (Sweden)

Tomoyuki Obuchi

Full Text Available We develop an approximation formula for the cross-validation error (CVE of a sparse linear regression penalized by ℓ1-norm and total variation terms, which is based on a perturbative expansion utilizing the largeness of both the data dimensionality and the model. The developed formula allows us to reduce the necessary computational cost of the CVE evaluation significantly. The practicality of the formula is tested through application to simulated black-hole image reconstruction on the event-horizon scale with super resolution. The results demonstrate that our approximation reproduces the CVE values obtained via literally conducted cross-validation with reasonably good precision.

Sparse linear models: Variational approximate inference and Bayesian experimental design

International Nuclear Information System (INIS)

Seeger, Matthias W

2009-01-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Sparse linear models: Variational approximate inference and Bayesian experimental design

Energy Technology Data Exchange (ETDEWEB)

Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

2009-12-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Analysis of interfraction and intrafraction variation during tangential breast irradiation with an electronic portal imaging device

International Nuclear Information System (INIS)

Smith, Ryan P.; Bloch, Peter; Harris, Eleanor E.; McDonough, James; Sarkar, Abhirup; Kassaee, Alireza; Avery, Steven; Solin, Lawrence J.

2005-01-01

Purpose: To evaluate the daily setup variation and the anatomic movement of the heart and lungs during breast irradiation with tangential photon beams, as measured with an electronic portal imaging device. Methods and materials: Analysis of 1,709 portal images determined changes in the radiation field during a treatment course in 8 patients. Values obtained for every image included central lung distance (CLD) and area of lung and heart within the irradiated field. The data from these measurements were used to evaluate variation from setup between treatment days and motion due to respiration and/or patient movement during treatment delivery. Results: The effect of respiratory motion and movement during treatment was minimal: the maximum range in CLD for any patient on any day was 0.25 cm. The variation caused by day-to-day setup variation was greater, with CLD values for patients ranging from 0.59 cm to 2.94 cm. Similar findings were found for heart and lung areas. Conclusions: There is very little change in CLD and corresponding lung and heart area during individual radiation treatment fractions in breast tangential fields, compared with a relatively greater amount of variation that occurs between days

Community patterns of tropical tree phenology derived from Unmanned Aerial Vehicle images: intra- and interspecific variation, association with species plant traits, and response to interannual climate variation

Science.gov (United States)

Bohlman, Stephanie; Rifai, Sami; Park, John; Dandois, Jonathan; Muller-Landau, Helene

2017-04-01

identities of 2000 crowns in the images by linking the crowns to stem tags in the field, thus producing a time series of cumulative annual deciduousness for 65 species. Deciduousness showed continuous variation among species rather than distinct phenological categories (ie evergreen and deciduous) that are commonly used in physiological, ecosystem and modeling studies. Some species labelled as evergreen by expert-based classification had annual deciduousness higher than those labelled as deciduous. We found significant, positive relationships between species mean deciduousness and species' leaf phosphorous, photosynthetic capacity and adult relative growth rate, suggesting that higher deciduousness is associated with greater resource acquisition. Comparing May 2015 (during an El Nino drought) and May 2014 (an non El Nino year with normal rainfall), mean deciduousness values for nearly all species was greater in 2015 but with differing levels of intraspecific variation. We discuss how the variation in deciduousness among species, its relationship with plant traits and response to the drought might be incorporated into terrestrial biosphere models of tropical forests to more accurately represent phenology and understand the consequences of community-level variation in phenology for ecosystem processes.

Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network

Directory of Open Access Journals (Sweden)

Haiqing He

2018-02-01

Full Text Available Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliable for complex background variations, because the gradient or other image grayscale information used to construct the feature descriptor is sensitive to image background variations. Recently, deep learning-based methods have been proven suitable for high-level feature representation and comparison in image matching. Inspired by the progresses made in deep learning, a new technical framework for remote sensing image matching based on the Siamese convolutional neural network is presented in this paper. First, a Siamese-type network architecture is designed to simultaneously learn the features and the corresponding similarity metric from labeled training examples of matching and non-matching true-color patch pairs. In the proposed network, two streams of convolutional and pooling layers sharing identical weights are arranged without the manually designed features. The number of convolutional layers is determined based on the factors that affect image matching. The sigmoid function is employed to compute the matching and non-matching probabilities in the output layer. Second, a gridding sub-pixel Harris algorithm is used to obtain the accurate localization of candidate matches. Third, a Gaussian pyramid coupling quadtree is adopted to gradually narrow down the searching space of the candidate matches, and multiscale patches are compared synchronously. Subsequently, a similarity measure based on the output of the sigmoid is adopted to find the initial matches. Finally, the random sample consensus algorithm and the whole-to-local quadratic polynomial constraints are used to remove false matches. In the experiments, different types of satellite datasets, such as ZY3, GF1, IKONOS, and Google Earth images

Imaging Tumor Variation in Response to Photodynamic Therapy in Pancreatic Cancer Xenograft Models

International Nuclear Information System (INIS)

Samkoe, Kimberley S.; Chen, Alina; Rizvi, Imran; O'Hara, Julia A.; Hoopes, P. Jack; Pereira, Stephen P.; Hasan, Tayyaba; Pogue, Brian W.

2010-01-01

Purpose: A treatment monitoring study investigated the differential effects of orthotopic pancreatic cancer models in response to interstitial photodynamic therapy (PDT), and the validity of using magnetic resonance imaging as a surrogate measure of response was assessed. Methods and Materials: Different orthotopic pancreatic cancer xenograft models (AsPC-1 and Panc-1) were used to represent the range of pathophysiology observed in human beings. Identical dose escalation studies (10, 20, and 40J/cm) using interstitial verteporfin PDT were performed, and magnetic resonance imaging with T2-weighted and T1-weighted contrast were used to monitor the total tumor volume and the vascular perfusion volume, respectively. Results: There was a significant amount of necrosis in the slower-growing Panc-1 tumor using high light dose, although complete necrosis was not observed. Lower doses were required for the same level of tumor kill in the faster-growing AsPC-1 cell line. Conclusions: The tumor growth rate and vascular pattern of the tumor affect the optimal PDT treatment regimen, with faster-growing tumors being relatively easier to treat. This highlights the fact that therapy in human beings shows a heterogeneous range of outcomes, and suggests a need for careful individualized treatment outcomes assessment in clinical work.

Spacial Variation in SAR Images of Different Resolution for Agricultural Fields

DEFF Research Database (Denmark)

Sandholt, Inge; Skriver, Henning

1999-01-01

The spatial variation in two types of Synthetic Aperture Radar (SAR) images covering agricultural fields is analysed. C-band polarimetric SAR data from the Danish airborne SAR, EMISAR, have been compared to space based ERS-1 C-band SAR with respect to scale and effect of polarization. The general...

Optimization image of magnetic resonance imaging (MRI) T2 fast spin echo (FSE) with variation echo train length (ETL) on the rupture tendon achilles case

International Nuclear Information System (INIS)

Muzamil, Akhmad; Firmansyah, Achmad Haries

2017-01-01

The research was done the optimization image of Magnetic Resonance Imaging (MRI) T2 Fast Spin Echo (FSE) with variation Echo Train Length (ETL) on the Rupture Tendon Achilles case. This study aims to find the variations Echo Train Length (ETL) from the results of ankle’s MRI image and find out how the value of Echo Train Length (ETL) works on the MRI ankle to produce optimal image. In this research, the used ETL variations were 12 and 20 with the interval 2 on weighting T2 FSE sagittal. The study obtained the influence of Echo Train Length (ETL) on the quality of ankle MRI image sagittal using T2 FSE weighting and analyzed in 25 images of five patients. The data analysis has done quantitatively with the Region of Interest (ROI) directly on computer MRI image planes which conducted statistical tests Signal to Noise Ratio (SNR) and Contras to Noise Ratio (CNR). The Signal to Noise Ratio (SNR) was the highest finding on fat tissue, while the Contras to Noise Ratio (CNR) on the Tendon-Fat tissue with ETL 12 found in two patients. The statistics test showed the significant SNR value of the 0.007 (p<0.05) of Tendon tissue, 0.364 (p>0.05) of the Fat, 0.912 (p>0.05) of the Fibula, and 0.436 (p>0.05) of the Heel Bone. For the contrast to noise ratio (CNR) of the Tendon-FAT tissue was about 0.041 (p>0.05). The results of the study showed that ETL variation with T2 FSE sagittal weighting had difference at Tendon tissue and Tendon-Fat tissue for MRI imaging quality. SNR and CNR were an important aspect on imaging optimization process to give the diagnose information. (paper)

Total variation superiorized conjugate gradient method for image reconstruction

Science.gov (United States)

Zibetti, Marcelo V. W.; Lin, Chuan; Herman, Gabor T.

2018-03-01

The conjugate gradient (CG) method is commonly used for the relatively-rapid solution of least squares problems. In image reconstruction, the problem can be ill-posed and also contaminated by noise; due to this, approaches such as regularization should be utilized. Total variation (TV) is a useful regularization penalty, frequently utilized in image reconstruction for generating images with sharp edges. When a non-quadratic norm is selected for regularization, as is the case for TV, then it is no longer possible to use CG. Non-linear CG is an alternative, but it does not share the efficiency that CG shows with least squares and methods such as fast iterative shrinkage-thresholding algorithms (FISTA) are preferred for problems with TV norm. A different approach to including prior information is superiorization. In this paper it is shown that the conjugate gradient method can be superiorized. Five different CG variants are proposed, including preconditioned CG. The CG methods superiorized by the total variation norm are presented and their performance in image reconstruction is demonstrated. It is illustrated that some of the proposed variants of the superiorized CG method can produce reconstructions of superior quality to those produced by FISTA and in less computational time, due to the speed of the original CG for least squares problems. In the Appendix we examine the behavior of one of the superiorized CG methods (we call it S-CG); one of its input parameters is a positive number ɛ. It is proved that, for any given ɛ that is greater than the half-squared-residual for the least squares solution, S-CG terminates in a finite number of steps with an output for which the half-squared-residual is less than or equal to ɛ. Importantly, it is also the case that the output will have a lower value of TV than what would be provided by unsuperiorized CG for the same value ɛ of the half-squared residual.

Omnigradient Based Total Variation Minimization for Enhanced Defocus Deblurring of Omnidirectional Images

Directory of Open Access Journals (Sweden)

Yongle Li

2014-01-01

Full Text Available We propose a new method of image restoration for catadioptric defocus blur using omnitotal variation (Omni-TV minimization based on omnigradient. Catadioptric omnidirectional imaging systems usually consist of conventional cameras and curved mirrors for capturing 360° field of view. The problem of catadioptric omnidirectional imaging defocus blur, which is caused by lens aperture and mirror curvature, becomes more severe when high resolution sensors and large apertures are used. In an omnidirectional image, two points near each other may not be close to one another in the 3D scene. Traditional gradient computation cannot be directly applied to omnidirectional image processing. Thus, omnigradient computing method combined with the characteristics of catadioptric omnidirectional imaging is proposed. Following this Omni-TV minimization is used as the constraint for deconvolution regularization, leading to the restoration of defocus blur in an omnidirectional image to obtain all sharp omnidirectional images. The proposed method is important for improving catadioptric omnidirectional imaging quality and promoting applications in related fields like omnidirectional video and image processing.

TU-CD-BRA-12: Coupling PET Image Restoration and Segmentation Using Variational Method with Multiple Regularizations

Energy Technology Data Exchange (ETDEWEB)

Li, L; Tan, S [Huazhong University of Science and Technology, Wuhan, Hubei (China); Lu, W [University of Maryland School of Medicine, Baltimore, MD (United States)

2015-06-15

Purpose: To propose a new variational method which couples image restoration with tumor segmentation for PET images using multiple regularizations. Methods: Partial volume effect (PVE) is a major degrading factor impacting tumor segmentation accuracy in PET imaging. The existing segmentation methods usually need to take prior calibrations to compensate PVE and they are highly system-dependent. Taking into account that image restoration and segmentation can promote each other and they are tightly coupled, we proposed a variational method to solve the two problems together. Our method integrated total variation (TV) semi-blind deconvolution and Mumford-Shah (MS) segmentation. The TV norm was used on edges to protect the edge information, and the L{sub 2} norm was used to avoid staircase effect in the no-edge area. The blur kernel was constrained to the Gaussian model parameterized by its variance and we assumed that the variances in the X-Y and Z directions are different. The energy functional was iteratively optimized by an alternate minimization algorithm. Segmentation performance was tested on eleven patients with non-Hodgkin’s lymphoma, and evaluated by Dice similarity index (DSI) and classification error (CE). For comparison, seven other widely used methods were also tested and evaluated. Results: The combination of TV and L{sub 2} regularizations effectively improved the segmentation accuracy. The average DSI increased by around 0.1 than using either the TV or the L{sub 2} norm. The proposed method was obviously superior to other tested methods. It has an average DSI and CE of 0.80 and 0.41, while the FCM method — the second best one — has only an average DSI and CE of 0.66 and 0.64. Conclusion: Coupling image restoration and segmentation can handle PVE and thus improves tumor segmentation accuracy in PET. Alternate use of TV and L2 regularizations can further improve the performance of the algorithm. This work was supported in part by National Natural

Detecting in-field variation in photosynthetic capacity of trangenically modifed plants with hyperspectral imaging.

Science.gov (United States)

Meacham, K.; Montes, C.; Pederson, T.; Wu, J.; Guan, K.; Bernacchi, C.

2017-12-01

Improved photosynthetic rates have been shown to increase crop biomass, making improved photosynthesis a focus for driving future grain yield increases. Improving the photosynthetic pathway offers opportunity to meet food demand, but requires high throughput measurement techniques to detect photosynthetic variation in natural accessions and transgenically modified plants. Gas exchange measurements are the most widely used method of measuring photosynthesis in field trials but this process is laborious and slow, and requires further modeling to estimate meaningful parameters and to upscale to the plot or canopy level. In field trials of tobacco with modifications made to the photosynthetic pathway, we infer the maximum carboxylation rate of Rubisco (Vcmax) and maximum electron transport rate (Jmax) and detect photosynthetic variation from hyperspectral imaging with a partial least squares regression technique. Ground-truth measurements from photosynthetic gas exchange, a full-range (400-2500nm) handheld spectroadiometer with leaf clip, hyperspectral indices, and extractions of leaf pigments support the model. The results from a range of wild-type cultivars and from genetically modified germplasm suggest that the opportunity for rapid selection of top performing genotypes from among thousands of plots. This research creates the opportunity to extend agroecosystem models from simplified "one-cultivar" generic parameterization to better represent a full suite of current and future crop cultivars for a wider range of environmental conditions.

Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation.

Science.gov (United States)

Jaberi, Ramin; Siavashpour, Zahra; Aghamiri, Mahmoud Reza; Kirisits, Christian; Ghaderi, Reza

2017-12-01

Intra-fractional organs at risk (OARs) deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT). The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT) of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR) brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs) based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan. A model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in 'organs-applicators', while maintaining target dose at the original level. There are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients' plans to be able to serve as a clinical tool.

Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation

Directory of Open Access Journals (Sweden)

Ramin Jaberi

2017-12-01

Full Text Available Purpose : Intra-fractional organs at risk (OARs deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT. The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Material and methods : Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan. Results : A model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in ‘organs-applicators’, while maintaining target dose at the original level. Conclusions : There are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients’ plans to be able to serve as a clinical tool.

Variational multiscale models for charge transport.

Science.gov (United States)

Wei, Guo-Wei; Zheng, Qiong; Chen, Zhan; Xia, Kelin

2012-01-01

This work presents a few variational multiscale models for charge transport in complex physical, chemical and biological systems and engineering devices, such as fuel cells, solar cells, battery cells, nanofluidics, transistors and ion channels. An essential ingredient of the present models, introduced in an earlier paper (Bulletin of Mathematical Biology, 72, 1562-1622, 2010), is the use of differential geometry theory of surfaces as a natural means to geometrically separate the macroscopic domain from the microscopic domain, meanwhile, dynamically couple discrete and continuum descriptions. Our main strategy is to construct the total energy functional of a charge transport system to encompass the polar and nonpolar free energies of solvation, and chemical potential related energy. By using the Euler-Lagrange variation, coupled Laplace-Beltrami and Poisson-Nernst-Planck (LB-PNP) equations are derived. The solution of the LB-PNP equations leads to the minimization of the total free energy, and explicit profiles of electrostatic potential and densities of charge species. To further reduce the computational complexity, the Boltzmann distribution obtained from the Poisson-Boltzmann (PB) equation is utilized to represent the densities of certain charge species so as to avoid the computationally expensive solution of some Nernst-Planck (NP) equations. Consequently, the coupled Laplace-Beltrami and Poisson-Boltzmann-Nernst-Planck (LB-PBNP) equations are proposed for charge transport in heterogeneous systems. A major emphasis of the present formulation is the consistency between equilibrium LB-PB theory and non-equilibrium LB-PNP theory at equilibrium. Another major emphasis is the capability of the reduced LB-PBNP model to fully recover the prediction of the LB-PNP model at non-equilibrium settings. To account for the fluid impact on the charge transport, we derive coupled Laplace-Beltrami, Poisson-Nernst-Planck and Navier-Stokes equations from the variational principle

Variational multiscale models for charge transport

Science.gov (United States)

Wei, Guo-Wei; Zheng, Qiong; Chen, Zhan; Xia, Kelin

2012-01-01

This work presents a few variational multiscale models for charge transport in complex physical, chemical and biological systems and engineering devices, such as fuel cells, solar cells, battery cells, nanofluidics, transistors and ion channels. An essential ingredient of the present models, introduced in an earlier paper (Bulletin of Mathematical Biology, 72, 1562-1622, 2010), is the use of differential geometry theory of surfaces as a natural means to geometrically separate the macroscopic domain from the microscopic domain, meanwhile, dynamically couple discrete and continuum descriptions. Our main strategy is to construct the total energy functional of a charge transport system to encompass the polar and nonpolar free energies of solvation, and chemical potential related energy. By using the Euler-Lagrange variation, coupled Laplace-Beltrami and Poisson-Nernst-Planck (LB-PNP) equations are derived. The solution of the LB-PNP equations leads to the minimization of the total free energy, and explicit profiles of electrostatic potential and densities of charge species. To further reduce the computational complexity, the Boltzmann distribution obtained from the Poisson-Boltzmann (PB) equation is utilized to represent the densities of certain charge species so as to avoid the computationally expensive solution of some Nernst-Planck (NP) equations. Consequently, the coupled Laplace-Beltrami and Poisson-Boltzmann-Nernst-Planck (LB-PBNP) equations are proposed for charge transport in heterogeneous systems. A major emphasis of the present formulation is the consistency between equilibrium LB-PB theory and non-equilibrium LB-PNP theory at equilibrium. Another major emphasis is the capability of the reduced LB-PBNP model to fully recover the prediction of the LB-PNP model at non-equilibrium settings. To account for the fluid impact on the charge transport, we derive coupled Laplace-Beltrami, Poisson-Nernst-Planck and Navier-Stokes equations from the variational principle

Post-modelling of images from a laser-induced wavy boiling front

Energy Technology Data Exchange (ETDEWEB)

Matti, R.S., E-mail: ramiz.matti@ltu.se [Luleå University of Technology, Department of Engineering Sciences and Mathematics, SE-971 87 Luleå (Sweden); University of Mosul, College of Engineering, Department of Mechanical Engineering, Mosul (Iraq); Kaplan, A.F.H. [Luleå University of Technology, Department of Engineering Sciences and Mathematics, SE-971 87 Luleå (Sweden)

2015-12-01

Highlights: • New method: post-modelling of high speed images from a laser-induced front. • From the images a wavy cavity and its absorption distribution is calculated. • Histograms enable additional statistical analysis and understanding. • Despite the complex topology the absorptivity is bound to 35–43%. • The new method visualizes valuable complementary information. - Abstract: Processes like laser keyhole welding, remote fusion laser cutting or laser drilling are governed by a highly dynamic wavy boiling front that was recently recorded by ultra-high speed imaging. A new approach has now been established by post-modelling of the high speed images. Based on the image greyscale and on a cavity model the three-dimensional front topology is reconstructed. As a second step the Fresnel absorptivity modulation across the wavy front is calculated, combined with the local projection of the laser beam. Frequency polygons enable additional analysis of the statistical variations of the properties across the front. Trends like shadow formation and time dependency can be studied, locally and for the whole front. Despite strong topology modulation in space and time, for lasers with 1 μm wavelength and steel the absorptivity is bounded to a narrow range of 35–43%, owing to its Fresnel characteristics.

Global manipulation of digital images can lead to variation in cytological diagnosis.

Science.gov (United States)

Prasad, H; Wanjari, Sangeeta; Parwani, Rajkumar

2011-03-31

With the adoption of a completely electronic workflow by several journals and the advent of telepathology, digital imaging has become an integral part of every scientific research. However, manipulating digital images is very easy, and it can lead to misinterpretations. To analyse the impact of manipulating digital images on their diagnosis. Digital images were obtained from Papanicolaou-stained smears of dysplastic and normal oral epithelium. They were manipulated using GNU Image Manipulation Program (GIMP) to alter their brightness and contrast and color levels. A Power Point presentation composed of slides of these manipulated images along with the unaltered originals arranged randomly was created. The presentation was shown to five observers individually who rated the images as normal, mild, moderate or severe dysplasia. Weighted κ statistics was used to measure and assess the levels of agreement between observers. Levels of agreement between manipulated images and original images varied greatly among observers. Variation in diagnosis was in the form of overdiagnosis or under-diagnosis, usually by one grade. Global manipulations of digital images of cytological slides can significantly affect their interpretation. Such manipulations should therefore be kept to a minimum, and avoided wherever possible.

NUFFT-Based Iterative Image Reconstruction via Alternating Direction Total Variation Minimization for Sparse-View CT

Directory of Open Access Journals (Sweden)

Bin Yan

2015-01-01

Full Text Available Sparse-view imaging is a promising scanning method which can reduce the radiation dose in X-ray computed tomography (CT. Reconstruction algorithm for sparse-view imaging system is of significant importance. The adoption of the spatial iterative algorithm for CT image reconstruction has a low operation efficiency and high computation requirement. A novel Fourier-based iterative reconstruction technique that utilizes nonuniform fast Fourier transform is presented in this study along with the advanced total variation (TV regularization for sparse-view CT. Combined with the alternating direction method, the proposed approach shows excellent efficiency and rapid convergence property. Numerical simulations and real data experiments are performed on a parallel beam CT. Experimental results validate that the proposed method has higher computational efficiency and better reconstruction quality than the conventional algorithms, such as simultaneous algebraic reconstruction technique using TV method and the alternating direction total variation minimization approach, with the same time duration. The proposed method appears to have extensive applications in X-ray CT imaging.

Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets

KAUST Repository

Lenzen, F.

2014-01-01

© 2014 Society for Industrial and Applied Mathematics. We consider a class of quasi-variational inequalities (QVIs) for adaptive image restoration, where the adaptivity is described via solution-dependent constraint sets. In previous work we studied both theoretical and numerical issues. While we were able to show the existence of solutions for a relatively broad class of problems, we encountered difficulties concerning uniqueness of the solution as well as convergence of existing algorithms for solving QVIs. In particular, it seemed that with increasing image size the growing condition number of the involved differential operator posed severe problems. In the present paper we prove uniqueness for a larger class of problems, particularly independent of the image size. Moreover, we provide a numerical algorithm with proved convergence. Experimental results support our theoretical findings.

The effects of internal refractive index variation in near-infrared optical tomography: a finite element modelling approach

International Nuclear Information System (INIS)

Dehghani, Hamid; Brooksby, Ben; Vishwanath, Karthik; Pogue, Brian W; Paulsen, Keith D

2003-01-01

Near-infrared (NIR) tomography is a technique used to measure light propagation through tissue and generate images of internal optical property distributions from boundary measurements. Most popular applications have concentrated on female breast imaging, neonatal and adult head imaging, as well as muscle and small animal studies. In most instances a highly scattering medium with a homogeneous refractive index is assumed throughout the imaging domain. Using these assumptions, it is possible to simplify the model to the diffusion approximation. However, biological tissue contains regions of varying optical absorption and scatter, as well as varying refractive index. In this work, we introduce an internal boundary constraint in the finite element method approach to modelling light propagation through tissue that accounts for regions of different refractive indices. We have compared the results to data from a Monte Carlo simulation and show that for a simple two-layered slab model of varying refractive index, the phase of the measured reflectance data is significantly altered by the variation in internal refractive index, whereas the amplitude data are affected only slightly

Limited data tomographic image reconstruction via dual formulation of total variation minimization

Science.gov (United States)

Jang, Kwang Eun; Sung, Younghun; Lee, Kangeui; Lee, Jongha; Cho, Seungryong

2011-03-01

The X-ray mammography is the primary imaging modality for breast cancer screening. For the dense breast, however, the mammogram is usually difficult to read due to tissue overlap problem caused by the superposition of normal tissues. The digital breast tomosynthesis (DBT) that measures several low dose projections over a limited angle range may be an alternative modality for breast imaging, since it allows the visualization of the cross-sectional information of breast. The DBT, however, may suffer from the aliasing artifact and the severe noise corruption. To overcome these problems, a total variation (TV) regularized statistical reconstruction algorithm is presented. Inspired by the dual formulation of TV minimization in denoising and deblurring problems, we derived a gradient-type algorithm based on statistical model of X-ray tomography. The objective function is comprised of a data fidelity term derived from the statistical model and a TV regularization term. The gradient of the objective function can be easily calculated using simple operations in terms of auxiliary variables. After a descending step, the data fidelity term is renewed in each iteration. Since the proposed algorithm can be implemented without sophisticated operations such as matrix inverse, it provides an efficient way to include the TV regularization in the statistical reconstruction method, which results in a fast and robust estimation for low dose projections over the limited angle range. Initial tests with an experimental DBT system confirmed our finding.

Joint reconstruction of dynamic PET activity and kinetic parametric images using total variation constrained dictionary sparse coding

Science.gov (United States)

Yu, Haiqing; Chen, Shuhang; Chen, Yunmei; Liu, Huafeng

2017-05-01

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 parametric images of the system macro parameters for tracer kinetics can be estimated. The proposed algorithm, which combines statistical data measurement and tracer kinetic models, integrates a dictionary sparse coding (DSC) into a total variational minimization based algorithm for simultaneous reconstruction of the activity distribution and parametric map from measured emission sinograms. DSC, based on the compartmental theory, provides biologically meaningful regularization, and total variation regularization is incorporated to provide edge-preserving guidance. We rely on techniques from minimization algorithms (the alternating direction method of multipliers) to first generate the estimated activity distributions with sub-optimal kinetic parameter estimates, and then recover the parametric maps given these activity estimates. These coupled iterative steps are repeated as necessary until convergence. Experiments with synthetic, Monte Carlo generated data, and real patient data have been conducted, and the results are very promising.

'When measurements mean action' decision models for portal image review to eliminate systematic set-up errors

International Nuclear Information System (INIS)

Wratten, C.R.; Denham, J.W.; O; Brien, P.; Hamilton, C.S.; Kron, T.; London Regional Cancer Centre, London, Ontario

2004-01-01

The aim of the present paper is to evaluate how the use of decision models in the review of portal images can eliminate systematic set-up errors during conformal therapy. Sixteen patients undergoing four-field irradiation of prostate cancer have had daily portal images obtained during the first two treatment weeks and weekly thereafter. The magnitude of random and systematic variations has been calculated by comparison of the portal image with the reference simulator images using the two-dimensional decision model embodied in the Hotelling's evaluation process (HEP). Random day-to-day set-up variation was small in this group of patients. Systematic errors were, however, common. In 15 of 16 patients, one or more errors of >2 mm were diagnosed at some stage during treatment. Sixteen of the 23 errors were between 2 and 4 mm. Although there were examples of oversensitivity of the HEP in three cases, and one instance of undersensitivity, the HEP proved highly sensitive to the small (2-4 mm) systematic errors that must be eliminated during high precision radiotherapy. The HEP has proven valuable in diagnosing very small ( 4 mm) systematic errors using one-dimensional decision models, HEP can eliminate the majority of systematic errors during the first 2 treatment weeks. Copyright (2004) Blackwell Science Pty Ltd

Inter-fraction variations in respiratory motion models

Energy Technology Data Exchange (ETDEWEB)

McClelland, J R; Modat, M; Ourselin, S; Hawkes, D J [Centre for Medical Image Computing, University College London (United Kingdom); Hughes, S; Qureshi, A; Ahmad, S; Landau, D B, E-mail: j.mcclelland@cs.ucl.ac.uk [Department of Oncology, Guy' s and St Thomas' s Hospitals NHS Trust, London (United Kingdom)

2011-01-07

Respiratory motion can vary dramatically between the planning stage and the different fractions of radiotherapy treatment. Motion predictions used when constructing the radiotherapy plan may be unsuitable for later fractions of treatment. This paper presents a methodology for constructing patient-specific respiratory motion models and uses these models to evaluate and analyse the inter-fraction variations in the respiratory motion. The internal respiratory motion is determined from the deformable registration of Cine CT data and related to a respiratory surrogate signal derived from 3D skin surface data. Three different models for relating the internal motion to the surrogate signal have been investigated in this work. Data were acquired from six lung cancer patients. Two full datasets were acquired for each patient, one before the course of radiotherapy treatment and one at the end (approximately 6 weeks later). Separate models were built for each dataset. All models could accurately predict the respiratory motion in the same dataset, but had large errors when predicting the motion in the other dataset. Analysis of the inter-fraction variations revealed that most variations were spatially varying base-line shifts, but changes to the anatomy and the motion trajectories were also observed.

Global manipulation of digital images can lead to variation in cytological diagnosis

Directory of Open Access Journals (Sweden)

H Prasad

2011-01-01

Full Text Available Background: With the adoption of a completely electronic workflow by several journals and the advent of telepathology, digital imaging has become an integral part of every scientific research. However, manipulating digital images is very easy, and it can lead to misinterpretations. Aim: To analyse the impact of manipulating digital images on their diagnosis. Design: Digital images were obtained from Papanicolaou-stained smears of dysplastic and normal oral epithelium. They were manipulated using GNU Image Manipulation Program (GIMP to alter their brightness and contrast and color levels. A Power Point presentation composed of slides of these manipulated images along with the unaltered originals arranged randomly was created. The presentation was shown to five observers individually who rated the images as normal, mild, moderate or severe dysplasia. Weighted k statistics was used to measure and assess the levels of agreement between observers. Results: Levels of agreement between manipulated images and original images varied greatly among observers. Variation in diagnosis was in the form of overdiagnosis or under-diagnosis, usually by one grade. Conclusion: Global manipulations of digital images of cytological slides can significantly affect their interpretation. Such manipulations should therefore be kept to a minimum, and avoided wherever possible.

Rectal dose variation during the course of image-guided radiation therapy of prostate cancer

International Nuclear Information System (INIS)

Chen Lili; Paskalev, Kamen; Xu Xiu; Zhu, Jennifer; Wang Lu; Price, Robert A.; Hu Wei; Feigenberg, Steven J.; Horwitz, Eric M.; Pollack, Alan; Charlie Ma, C.M.

2010-01-01

Background and purpose: To investigate the change in rectal dose during the treatment course for intensity-modulated radiotherapy (IMRT) of prostate cancer with image-guidance. Materials and methods: Twenty prostate cancer patients were recruited for this retrospective study. All patients have been treated with IMRT. For each patient, MR and CT images were fused for target and critical structure delineation. IMRT treatment planning was performed on the simulation CT images. Inter-fractional motion during the course of treatment was corrected using a CT-on-rails system. The rectum was outlined on both the original treatment plan and the subsequent daily CT images from the CT-on-rails by the same investigator. Dose distributions on these daily CT images were recalculated with the isocenter shifts relative to the simulation CT images using the leaf sequences/MUs based on the original treatment plan. The rectal doses from the subsequent daily CTs were compared with the original doses planned on the simulation CT using our clinical acceptance criteria. Results: Based on 20 patients with 139 daily CT sets, 28% of the subsequent treatment dose distributions did not meet our criterion of V 40 65 < 17%. The inter-fractional rectal volume variation is significant for some patients. Conclusions: Due to the large inter-fractional variation of the rectal volume, it is more favorable to plan prostate IMRT based on an empty rectum and deliver treatment to patients with an empty rectum. Over 70% of actual treatments showed better rectal doses than our clinical acceptance criteria. A significant fraction (27%) of the actual treatments would benefit from adaptive image-guided radiotherapy based on daily CT images.

Variation in the quality of CT images of the upper abdomen when CT automatic exposure control is employed

International Nuclear Information System (INIS)

Aizawa, Isao; Muramatsu, Yoshihisa; Nomura, Keiichi; Shimizu, Fuminori

2010-01-01

The aim of this study was to analyze the reason for variation of image quality in the upper abdomen CT with the use of CT-automatic exposure control (AEC). The CT investigated was 3D modulation in the 16 multi detector row CT (MDCT) and lung cancer screening CT (LSCT) phantom was used to simulate the patient. When there was a phase difference, an image noise increase of around 15% at the maximum was accepted. It is concluded that the major reason for variation in image quality is respiratory motion and the importance of respiration control must be recognized. (author)

Minimizing EIT image artefacts from mesh variability in finite element models.

Science.gov (United States)

Adler, Andy; Lionheart, William R B

2011-07-01

Electrical impedance tomography (EIT) solves an inverse problem to estimate the conductivity distribution within a body from electrical simulation and measurements at the body surface, where the inverse problem is based on a solution of Laplace's equation in the body. Most commonly, a finite element model (FEM) is used, largely because of its ability to describe irregular body shapes. In this paper, we show that simulated variations in the positions of internal nodes within a FEM can result in serious image artefacts in the reconstructed images. Such variations occur when designing FEM meshes to conform to conductivity targets, but the effects may also be seen in other applications of absolute and difference EIT. We explore the hypothesis that these artefacts result from changes in the projection of the anisotropic conductivity tensor onto the FEM system matrix, which introduces anisotropic components into the simulated voltages, which cannot be reconstructed onto an isotropic image, and appear as artefacts. The magnitude of the anisotropic effect is analysed for a small regular FEM, and shown to be proportional to the relative node movement as a fraction of element size. In order to address this problem, we show that it is possible to incorporate a FEM node movement component into the formulation of the inverse problem. These results suggest that it is important to consider artefacts due to FEM mesh geometry in EIT image reconstruction.

A variational void coalescence model for ductile metals

KAUST Repository

Siddiq, Amir

2011-08-17

We present a variational void coalescence model that includes all the essential ingredients of failure in ductile porous metals. The model is an extension of the variational void growth model by Weinberg et al. (Comput Mech 37:142-152, 2006). The extended model contains all the deformation phases in ductile porous materials, i.e. elastic deformation, plastic deformation including deviatoric and volumetric (void growth) plasticity followed by damage initiation and evolution due to void coalescence. Parametric studies have been performed to assess the model\\'s dependence on the different input parameters. The model is then validated against uniaxial loading experiments for different materials. We finally show the model\\'s ability to predict the damage mechanisms and fracture surface profile of a notched round bar under tension as observed in experiments. © Springer-Verlag 2011.

A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data.

Science.gov (United States)

Luo, Jianhua; Mou, Zhiying; Qin, Binjie; Li, Wanqing; Ogunbona, Philip; Robini, Marc C; Zhu, Yuemin

2017-12-09

Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of an image in the k-space, the singular k-space model (SKM) is proposed in terms of the k-space functions of a singularizing operator. The singularizing operator is constructed by combining basic difference operators. An algorithm is developed to reliably estimate the model parameters from undersampled k-space data. The estimated parameters are then used to recover the missing k-space data through the model, subsequently achieving high-quality reconstruction of the image using inverse Fourier transform. Experiments on physical phantom and real brain MR images have shown that the proposed SKM method constantly outperforms the popular total variation (TV) and the classical zero-filling (ZF) methods regardless of the undersampling rates, the noise levels, and the image structures. For the same objective quality of the reconstructed images, the proposed method requires much less k-space data than the TV method. The SKM method is an effective method for fast MRI reconstruction from the undersampled k-space data. Graphical abstract Two Real Images and their sparsified images by singularizing operator.

Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method

Science.gov (United States)

D'Ambra, Pasqua; Tartaglione, Gaetano

2015-03-01

Image segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology.

Image analysis and modeling in medical image computing. Recent developments and advances.

Science.gov (United States)

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body

Delay Variation Model with Two Service Queues

Directory of Open Access Journals (Sweden)

Filip Rezac

2010-01-01

Full Text Available Delay in VoIP technology is very unpleasant issue and therefore a voice packets prioritization must be ensured. To maintain the high call quality a maximum information delivery time from the sender to the recipient is set to 150 ms. This paper focuses on the design of a mathematical model of end-to-end delay of a VoIP connection, in particular on a delay variation. It describes all partial delay components and mechanisms, their generation, facilities and mathematical formulations. A new approach to the delay variation model is presented and its validation has been done by experimention.

Study of Colour Model for Segmenting Mycobacterium Tuberculosis in Sputum Images

Science.gov (United States)

Kurniawardhani, A.; Kurniawan, R.; Muhimmah, I.; Kusumadewi, S.

2018-03-01

One of method to diagnose Tuberculosis (TB) disease is sputum test. The presence and number of Mycobacterium tuberculosis (MTB) in sputum are identified. The presence of MTB can be seen under light microscope. Before investigating through stained light microscope, the sputum samples are stained using Ziehl-Neelsen (ZN) stain technique. Because there is no standard procedure in staining, the appearance of sputum samples may vary either in background colour or contrast level. It increases the difficulty in segmentation stage of automatic MTB identification. Thus, this study investigated the colour models to look for colour channels of colour model that can segment MTB well in different stained conditions. The colour models will be investigated are each channel in RGB, HSV, CIELAB, YCbCr, and C-Y colour model and the clustering algorithm used is k-Means. The sputum image dataset used in this study is obtained from community health clinic in a district in Indonesia. The size of each image was set to 1600x1200 pixels which is having variation in number of MTB, background colour, and contrast level. The experiment result indicates that in all image conditions, blue, hue, Cr, and Ry colour channel can be used to segment MTB in one cluster well.

Impact of the Parameter Variation on the Image Blurring in 3 T Magnetic Resonance Imaging: A Phantom Study

Energy Technology Data Exchange (ETDEWEB)

Kim, Jong Woo; Lee, Sang Hoon; Kim, Nam Kug; Cho, Kyung Sik; Lee, Jin Seong [Dept. of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of)

2013-04-15

To evaluate the effects of the key imaging-parameter alterations on the four MR sequences in a phantom study. Magnetic resonance (MR) imaging was performed on a MR phantom with an 8-channel head coil by using a 3 T MR system. The images were obtained in the axial plane on four MR sequences [T1-weighted, T2-weighted, Proton-density, and 3 dimensional (3D) fast spin echo (FSE)] with controlled variations in the following key parameters: 1) echo train length (ETL), 2) repetition time (TR), and 3) echo time (TE). The image blurring was determined by the degree of the gradient angle; i.e., the blurring increased as the gradient angle decreases. The increasing ETL was observed to cause an increase in the image blurring on all pulse sequences with a statistical significance (p = 0.004) on the 3D FSE. Increasing the TR appeared to have no effect except a statistically significant decrease on the T1-weighted images (p = 0.011). Increasing TE showed no effect on the T1-weighted images (p = 0.932); however, it caused an increase of blurring on the proton density images (p = 0.016) as well as the T2-weighted images (p < 0.001), and a decrease on the 3D FSE (p = 0.001). To reduce the image blurring, short ETL and long TE for 3D FSE, long TR for T1-weighted images and short TE for proton-density and T2-weighted images should be applied.

Image sequence analysis in nuclear medicine: (1) Parametric imaging using statistical modelling

International Nuclear Information System (INIS)

Liehn, J.C.; Hannequin, P.; Valeyre, J.

1989-01-01

This is a review of parametric imaging methods on Nuclear Medicine. A Parametric Image is an image in which each pixel value is a function of the value of the same pixel of an image sequence. The Local Model Method is the fitting of each pixel time activity curve by a model which parameter values form the Parametric Images. The Global Model Method is the modelling of the changes between two images. It is applied to image comparison. For both methods, the different models, the identification criterion, the optimization methods and the statistical properties of the images are discussed. The analysis of one or more Parametric Images is performed using 1D or 2D histograms. The statistically significant Parametric Images, (Images of significant Variances, Amplitudes and Differences) are also proposed [fr

Procedural facade variations from a single layout

KAUST Repository

Bao, Fan

2013-02-19

We introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size. Computing such new facade variations has many unique challenges, and we propose a new algorithm based on interleaving heuristic search and quadratic programming. In contrast to most previous work, we focus on the generation of new design variations and not on the automatic analysis of the input\\'s structure. Adding a modeling step with the user in the loop ensures that our results routinely are of high quality. © 2013 ACM.

Procedural facade variations from a single layout

KAUST Repository

Bao, Fan; Schwarz, Michael; Wonka, Peter

2013-01-01

We introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size. Computing such new facade variations has many unique challenges, and we propose a new algorithm based on interleaving heuristic search and quadratic programming. In contrast to most previous work, we focus on the generation of new design variations and not on the automatic analysis of the input's structure. Adding a modeling step with the user in the loop ensures that our results routinely are of high quality. © 2013 ACM.

Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction.

Science.gov (United States)

Liu, Yan; Ma, Jianhua; Fan, Yi; Liang, Zhengrong

2012-12-07

Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and other constraints, piecewise-smooth x-ray computed tomography (CT) can be reconstructed from sparse-view projection data without introducing notable artifacts. However, due to the piecewise constant assumption for the image, a conventional TV minimization algorithm often suffers from over-smoothness on the edges of the resulting image. To mitigate this drawback, we present an adaptive-weighted TV (AwTV) minimization algorithm in this paper. The presented AwTV model is derived by considering the anisotropic edge property among neighboring image voxels, where the associated weights are expressed as an exponential function and can be adaptively adjusted by the local image-intensity gradient for the purpose of preserving the edge details. Inspired by the previously reported TV-POCS (projection onto convex sets) implementation, a similar AwTV-POCS implementation was developed to minimize the AwTV subject to data and other constraints for the purpose of sparse-view low-dose CT image reconstruction. To evaluate the presented AwTV-POCS algorithm, both qualitative and quantitative studies were performed by computer simulations and phantom experiments. The results show that the presented AwTV-POCS algorithm can yield images with several notable gains, in terms of noise-resolution tradeoff plots and full-width at half-maximum values, as compared to the corresponding conventional TV-POCS algorithm.

Introductory Biology Students’ Conceptual Models and Explanations of the Origin of Variation

Science.gov (United States)

Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

2014-01-01

Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess understanding of the origin of variation. By midterm, only a small percentage of students articulated complete and accurate representations of the origin of variation in their models. Targeted feedback was offered through activities requiring students to critically evaluate peers’ models. At semester's end, a substantial proportion of students significantly improved their representation of how variation arises (though one-third still did not include mutation in their models). Students’ written explanations of the origin of variation were mostly consistent with their models, although less effective than models in conveying mechanistic reasoning. This study contributes evidence that articulating the genetic origin of variation is particularly challenging for learners and may require multiple cycles of instruction, assessment, and feedback. To support meaningful learning of the origin of variation, we advocate instruction that explicitly integrates multiple scales of biological organization, assessment that promotes and reveals mechanistic and causal reasoning, and practice with explanatory models with formative feedback. PMID:25185235

Image correction during large and rapid B(0) variations in an open MRI system with permanent magnets using navigator echoes and phase compensation.

Science.gov (United States)

Li, Jianqi; Wang, Yi; Jiang, Yu; Xie, Haibin; Li, Gengying

2009-09-01

An open permanent magnet system with vertical B(0) field and without self-shielding can be quite susceptible to perturbations from external magnetic sources. B(0) variation in such a system located close to a subway station was measured to be greater than 0.7 microT by both MRI and a fluxgate magnetometer. This B(0) variation caused image artifacts. A navigator echo approach that monitored and compensated the view-to-view variation in magnetic resonance signal phase was developed to correct for image artifacts. Human brain imaging experiments using a multislice gradient-echo sequence demonstrated that the ghosting and blurring artifacts associated with B(0) variations were effectively removed using the navigator method.

Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

Science.gov (United States)

Nabizadeh, Nooshin; John, Nigel

2014-03-01

Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

Effect of camera temperature variations on stereo-digital image correlation measurements

KAUST Repository

Pan, Bing

2015-11-25

In laboratory and especially non-laboratory stereo-digital image correlation (stereo-DIC) applications, the extrinsic and intrinsic parameters of the cameras used in the system may change slightly due to the camera warm-up effect and possible variations in ambient temperature. Because these camera parameters are generally calibrated once prior to measurements and considered to be unaltered during the whole measurement period, the changes in these parameters unavoidably induce displacement/strain errors. In this study, the effect of temperature variations on stereo-DIC measurements is investigated experimentally. To quantify the errors associated with camera or ambient temperature changes, surface displacements and strains of a stationary optical quartz glass plate with near-zero thermal expansion were continuously measured using a regular stereo-DIC system. The results confirm that (1) temperature variations in the cameras and ambient environment have a considerable influence on the displacements and strains measured by stereo-DIC due to the slightly altered extrinsic and intrinsic camera parameters; and (2) the corresponding displacement and strain errors correlate with temperature changes. For the specific stereo-DIC configuration used in this work, the temperature-induced strain errors were estimated to be approximately 30–50 με/°C. To minimize the adverse effect of camera temperature variations on stereo-DIC measurements, two simple but effective solutions are suggested.

Effect of camera temperature variations on stereo-digital image correlation measurements

KAUST Repository

Pan, Bing; Shi, Wentao; Lubineau, Gilles

2015-01-01

In laboratory and especially non-laboratory stereo-digital image correlation (stereo-DIC) applications, the extrinsic and intrinsic parameters of the cameras used in the system may change slightly due to the camera warm-up effect and possible variations in ambient temperature. Because these camera parameters are generally calibrated once prior to measurements and considered to be unaltered during the whole measurement period, the changes in these parameters unavoidably induce displacement/strain errors. In this study, the effect of temperature variations on stereo-DIC measurements is investigated experimentally. To quantify the errors associated with camera or ambient temperature changes, surface displacements and strains of a stationary optical quartz glass plate with near-zero thermal expansion were continuously measured using a regular stereo-DIC system. The results confirm that (1) temperature variations in the cameras and ambient environment have a considerable influence on the displacements and strains measured by stereo-DIC due to the slightly altered extrinsic and intrinsic camera parameters; and (2) the corresponding displacement and strain errors correlate with temperature changes. For the specific stereo-DIC configuration used in this work, the temperature-induced strain errors were estimated to be approximately 30–50 με/°C. To minimize the adverse effect of camera temperature variations on stereo-DIC measurements, two simple but effective solutions are suggested.

Dynamic contrast-enhanced MR imaging to assess physiologic variations of myometrial perfusion

Energy Technology Data Exchange (ETDEWEB)

Thomassin-Naggara, Isabelle [Assistance Publique-Hopitaux de Paris, Department of Radiology, Hopital Tenon, Paris (France); Universite Rene Descartes, Laboratoire de Recherche en Imagerie-INSERM U970, Paris (France); Hopital Tenon, Service de Radiologie, Paris (France); Balvay, Daniel [Universite Rene Descartes, Laboratoire de Recherche en Imagerie-INSERM U970, Paris (France); Cuenod, Charles A. [Universite Rene Descartes, Laboratoire de Recherche en Imagerie-INSERM U970, Paris (France); Hopital Europeen Georges Pompidou (HEGP), Department of Radiology, Paris (France); Darai, Emile [Assistance Publique-Hopitaux de Paris, Department of Gynaecology-Obstetrics, Hopital Tenon, Paris (France); Marsault, Claude; Bazot, Marc [Assistance Publique-Hopitaux de Paris, Department of Radiology, Hopital Tenon, Paris (France)

2010-04-15

To prospectively evaluate the ability of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess physiological microvascular states in normal myometrium. Eighty-five women (62 women of reproductive age, 23 postmenopausal) undergoing DCE-MRI of the pelvis were included. Microvascular parameters for the inner and outer myometrium were analysed using a pharmacokinetic model. These parameters were tissue blood flow (F), blood volume fraction (V{sub b}), permeability-surface area product (PS), interstitial volume fraction (V{sub e}) and lag time (Dt). In the women of reproductive age, the inner myometrium displayed higher F and PS, lower V{sub b} and V{sub e}, and longer Dt than the outer myometrium (p = 0.02, p = 0.01, p = 0.005, p = 0.03 and p = 0.01, respectively). The inner myometrium presented microvascular variations during the menstrual cycle with a pre-ovulatory peak followed by a fall reaching a nadir of F and V{sub b} about 4 days after ovulation. Compared with women of reproductive age, in the postmenopausal state, F and V{sub b} decreased in the outer myometrium, while PS, V{sub e} and Dt increased (p < 0.0001, p = 0.001, p = 0.001, p = 0.03 and p = 0.0004, respectively). DCE-MRI is a non-invasive technique that can measure variations of myometrial microcirculation, and thereby be potentially useful to help characterize the role and states of the myometrium in assisted reproductive therapy. (orig.)

Dynamic contrast-enhanced MR imaging to assess physiologic variations of myometrial perfusion

International Nuclear Information System (INIS)

Thomassin-Naggara, Isabelle; Balvay, Daniel; Cuenod, Charles A.; Darai, Emile; Marsault, Claude; Bazot, Marc

2010-01-01

To prospectively evaluate the ability of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess physiological microvascular states in normal myometrium. Eighty-five women (62 women of reproductive age, 23 postmenopausal) undergoing DCE-MRI of the pelvis were included. Microvascular parameters for the inner and outer myometrium were analysed using a pharmacokinetic model. These parameters were tissue blood flow (F), blood volume fraction (V b ), permeability-surface area product (PS), interstitial volume fraction (V e ) and lag time (Dt). In the women of reproductive age, the inner myometrium displayed higher F and PS, lower V b and V e , and longer Dt than the outer myometrium (p = 0.02, p = 0.01, p = 0.005, p = 0.03 and p = 0.01, respectively). The inner myometrium presented microvascular variations during the menstrual cycle with a pre-ovulatory peak followed by a fall reaching a nadir of F and V b about 4 days after ovulation. Compared with women of reproductive age, in the postmenopausal state, F and V b decreased in the outer myometrium, while PS, V e and Dt increased (p < 0.0001, p = 0.001, p = 0.001, p = 0.03 and p = 0.0004, respectively). DCE-MRI is a non-invasive technique that can measure variations of myometrial microcirculation, and thereby be potentially useful to help characterize the role and states of the myometrium in assisted reproductive therapy. (orig.)

Image-based Modeling of PSF Deformation with Application to Limited Angle PET Data

Science.gov (United States)

Matej, Samuel; Li, Yusheng; Panetta, Joseph; Karp, Joel S.; Surti, Suleman

2016-01-01

The point-spread-functions (PSFs) of reconstructed images can be deformed due to detector effects such as resolution blurring and parallax error, data acquisition geometry such as insufficient sampling or limited angular coverage in dual-panel PET systems, or reconstruction imperfections/simplifications. PSF deformation decreases quantitative accuracy and its spatial variation lowers consistency of lesion uptake measurement across the imaging field-of-view (FOV). This can be a significant problem with dual panel PET systems even when using TOF data and image reconstruction models of the detector and data acquisition process. To correct for the spatially variant reconstructed PSF distortions we propose to use an image-based resolution model (IRM) that includes such image PSF deformation effects. Originally the IRM was mostly used for approximating data resolution effects of standard PET systems with full angular coverage in a computationally efficient way, but recently it was also used to mitigate effects of simplified geometric projectors. Our work goes beyond this by including into the IRM reconstruction imperfections caused by combination of the limited angle, parallax errors, and any other (residual) deformation effects and testing it for challenging dual panel data with strongly asymmetric and variable PSF deformations. We applied and tested these concepts using simulated data based on our design for a dedicated breast imaging geometry (B-PET) consisting of dual-panel, time-of-flight (TOF) detectors. We compared two image-based resolution models; i) a simple spatially invariant approximation to PSF deformation, which captures only the general PSF shape through an elongated 3D Gaussian function, and ii) a spatially variant model using a Gaussian mixture model (GMM) to more accurately capture the asymmetric PSF shape in images reconstructed from data acquired with the B-PET scanner geometry. Results demonstrate that while both IRMs decrease the overall uptake

Modeling response variation for radiometric calorimeters

International Nuclear Information System (INIS)

Mayer, R.L. II.

1986-01-01

Radiometric calorimeters are widely used in the DOE complex for accountability measurements of plutonium and tritium. Proper characterization of response variation for these instruments is, therefore, vital for accurate assessment of measurement control as well as for propagation of error calculations. This is not difficult for instruments used to measure items within a narrow range of power values; however, when a single instrument is used to measure items over a wide range of power values, improper estimates of uncertainty can result since traditional error models for radiometric calorimeters assume that uncertainty is not a function of sample power. This paper describes methods which can be used to accurately estimate random response variation for calorimeters used to measure items over a wide range of sample powers. The model is applicable to the two most common modes of calorimeter operation: heater replacement and servo control. 5 refs., 4 figs., 1 tab

Aircraft Segmentation in SAR Images Based on Improved Active Shape Model

Science.gov (United States)

Zhang, X.; Xiong, B.; Kuang, G.

2018-04-01

In SAR image interpretation, aircrafts are the important targets arousing much attention. However, it is far from easy to segment an aircraft from the background completely and precisely in SAR images. Because of the complex structure, different kinds of electromagnetic scattering take place on the aircraft surfaces. As a result, aircraft targets usually appear to be inhomogeneous and disconnected. It is a good idea to extract an aircraft target by the active shape model (ASM), since combination of the geometric information controls variations of the shape during the contour evolution. However, linear dimensionality reduction, used in classic ACM, makes the model rigid. It brings much trouble to segment different types of aircrafts. Aiming at this problem, an improved ACM based on ISOMAP is proposed in this paper. ISOMAP algorithm is used to extract the shape information of the training set and make the model flexible enough to deal with different aircrafts. The experiments based on real SAR data shows that the proposed method achieves obvious improvement in accuracy.

Automated drusen detection in retinal images using analytical modelling algorithms

Directory of Open Access Journals (Sweden)

Manivannan Ayyakkannu

2011-07-01

Full Text Available Abstract Background Drusen are common features in the ageing macula associated with exudative Age-Related Macular Degeneration (ARMD. They are visible in retinal images and their quantitative analysis is important in the follow up of the ARMD. However, their evaluation is fastidious and difficult to reproduce when performed manually. Methods This article proposes a methodology for Automatic Drusen Deposits Detection and quantification in Retinal Images (AD3RI by using digital image processing techniques. It includes an image pre-processing method to correct the uneven illumination and to normalize the intensity contrast with smoothing splines. The drusen detection uses a gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. The detected drusen are then fitted by Modified Gaussian functions, producing a model of the image that is used to evaluate the affected area. Twenty two images were graded by eight experts, with the aid of a custom made software and compared with AD3RI. This comparison was based both on the total area and on the pixel-to-pixel analysis. The coefficient of variation, the intraclass correlation coefficient, the sensitivity, the specificity and the kappa coefficient were calculated. Results The ground truth used in this study was the experts' average grading. In order to evaluate the proposed methodology three indicators were defined: AD3RI compared to the ground truth (A2G; each expert compared to the other experts (E2E and a standard Global Threshold method compared to the ground truth (T2G. The results obtained for the three indicators, A2G, E2E and T2G, were: coefficient of variation 28.8 %, 22.5 % and 41.1 %, intraclass correlation coefficient 0.92, 0.88 and 0.67, sensitivity 0.68, 0.67 and 0.74, specificity 0.96, 0.97 and 0.94, and kappa coefficient 0.58, 0.60 and 0.49, respectively. Conclusions The gradings produced by AD3RI obtained an agreement

A generalized model for optimal transport of images including dissipation and density modulation

KAUST Repository

Maas, Jan

2015-11-01

© EDP Sciences, SMAI 2015. In this paper the optimal transport and the metamorphosis perspectives are combined. For a pair of given input images geodesic paths in the space of images are defined as minimizers of a resulting path energy. To this end, the underlying Riemannian metric measures the rate of transport cost and the rate of viscous dissipation. Furthermore, the model is capable to deal with strongly varying image contrast and explicitly allows for sources and sinks in the transport equations which are incorporated in the metric related to the metamorphosis approach by Trouvé and Younes. In the non-viscous case with source term existence of geodesic paths is proven in the space of measures. The proposed model is explored on the range from merely optimal transport to strongly dissipative dynamics. For this model a robust and effective variational time discretization of geodesic paths is proposed. This requires to minimize a discrete path energy consisting of a sum of consecutive image matching functionals. These functionals are defined on corresponding pairs of intensity functions and on associated pairwise matching deformations. Existence of time discrete geodesics is demonstrated. Furthermore, a finite element implementation is proposed and applied to instructive test cases and to real images. In the non-viscous case this is compared to the algorithm proposed by Benamou and Brenier including a discretization of the source term. Finally, the model is generalized to define discrete weighted barycentres with applications to textures and objects.

Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

Science.gov (United States)

Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

2014-09-01

Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. Copyright © 2014 Elsevier Inc. All rights reserved.

Radionuclide reporter gene imaging

Energy Technology Data Exchange (ETDEWEB)

Min, Jung Joon [School of Medicine, Chonnam National Univ., Gwangju (Korea, Republic of)

2004-04-01

Recent progress in the development of non-invasive imaging technologies continues to strengthen the role of molecular imaging biological research. These tools have been validated recently in variety of research models, and have been shown to provide continuous quantitative monitoring of the location(s), magnitude, and time-variation of gene expression. This article reviews the principles, characteristics, categories and the use of radionuclide reporter gene imaging technologies as they have been used in imaging cell trafficking, imaging gene therapy, imaging endogenous gene expression and imaging molecular interactions. The studies published to date demonstrate that reporter gene imaging technologies will help to accelerate model validation as well as allow for clinical monitoring of human diseases.

Radionuclide reporter gene imaging

International Nuclear Information System (INIS)

Min, Jung Joon

2004-01-01

Recent progress in the development of non-invasive imaging technologies continues to strengthen the role of molecular imaging biological research. These tools have been validated recently in variety of research models, and have been shown to provide continuous quantitative monitoring of the location(s), magnitude, and time-variation of gene expression. This article reviews the principles, characteristics, categories and the use of radionuclide reporter gene imaging technologies as they have been used in imaging cell trafficking, imaging gene therapy, imaging endogenous gene expression and imaging molecular interactions. The studies published to date demonstrate that reporter gene imaging technologies will help to accelerate model validation as well as allow for clinical monitoring of human diseases

The model of illumination-transillumination for image enhancement of X-ray images

Energy Technology Data Exchange (ETDEWEB)

Lyu, Kwang Yeul [Shingu College, Sungnam (Korea, Republic of); Rhee, Sang Min [Kangwon National Univ., Chuncheon (Korea, Republic of)

2001-06-01

In digital image processing, the homomorphic filtering approach is derived from an illumination - reflectance model of the image. It can also be used with an illumination-transillumination model X-ray film. Several X-ray images were applied to enhancement with histogram equalization and homomorphic filter based on an illumination-transillumination model. The homomorphic filter has proven theoretical claim of image density range compression and balanced contrast enhancement, and also was found a valuable tool to process analog X-ray images to digital images.

Modeling Per Capita State Health Expenditure Variat...

Data.gov (United States)

U.S. Department of Health & Human Services — Modeling Per Capita State Health Expenditure Variation State-Level Characteristics Matter, published in Volume 3, Issue 4, of the Medicare and Medicaid Research...

Efficient generation of image chips for training deep learning algorithms

Science.gov (United States)

Han, Sanghui; Fafard, Alex; Kerekes, John; Gartley, Michael; Ientilucci, Emmett; Savakis, Andreas; Law, Charles; Parhan, Jason; Turek, Matt; Fieldhouse, Keith; Rovito, Todd

2017-05-01

Training deep convolutional networks for satellite or aerial image analysis often requires a large amount of training data. For a more robust algorithm, training data need to have variations not only in the background and target, but also radiometric variations in the image such as shadowing, illumination changes, atmospheric conditions, and imaging platforms with different collection geometry. Data augmentation is a commonly used approach to generating additional training data. However, this approach is often insufficient in accounting for real world changes in lighting, location or viewpoint outside of the collection geometry. Alternatively, image simulation can be an efficient way to augment training data that incorporates all these variations, such as changing backgrounds, that may be encountered in real data. The Digital Imaging and Remote Sensing Image Image Generation (DIRSIG) model is a tool that produces synthetic imagery using a suite of physics-based radiation propagation modules. DIRSIG can simulate images taken from different sensors with variation in collection geometry, spectral response, solar elevation and angle, atmospheric models, target, and background. Simulation of Urban Mobility (SUMO) is a multi-modal traffic simulation tool that explicitly models vehicles that move through a given road network. The output of the SUMO model was incorporated into DIRSIG to generate scenes with moving vehicles. The same approach was used when using helicopters as targets, but with slight modifications. Using the combination of DIRSIG and SUMO, we quickly generated many small images, with the target at the center with different backgrounds. The simulations generated images with vehicles and helicopters as targets, and corresponding images without targets. Using parallel computing, 120,000 training images were generated in about an hour. Some preliminary results show an improvement in the deep learning algorithm when real image training data are augmented with

Equilibrium models and variational inequalities

CERN Document Server

Konnov, Igor

2007-01-01

The concept of equilibrium plays a central role in various applied sciences, such as physics (especially, mechanics), economics, engineering, transportation, sociology, chemistry, biology and other fields. If one can formulate the equilibrium problem in the form of a mathematical model, solutions of the corresponding problem can be used for forecasting the future behavior of very complex systems and, also, for correcting the the current state of the system under control. This book presents a unifying look on different equilibrium concepts in economics, including several models from related sciences.- Presents a unifying look on different equilibrium concepts and also the present state of investigations in this field- Describes static and dynamic input-output models, Walras, Cassel-Wald, spatial price, auction market, oligopolistic equilibrium models, transportation and migration equilibrium models- Covers the basics of theory and solution methods both for the complementarity and variational inequality probl...

Modelling the Probability Density Function of IPTV Traffic Packet Delay Variation

Directory of Open Access Journals (Sweden)

Michal Halas

2012-01-01

Full Text Available This article deals with modelling the Probability density function of IPTV traffic packet delay variation. The use of this modelling is in an efficient de-jitter buffer estimation. When an IP packet travels across a network, it experiences delay and its variation. This variation is caused by routing, queueing systems and other influences like the processing delay of the network nodes. When we try to separate these at least three types of delay variation, we need a way to measure these types separately. This work is aimed to the delay variation caused by queueing systems which has the main implications to the form of the Probability density function.

Evaluation of a compartmental model for estimating tumor hypoxia via FMISO dynamic PET imaging

International Nuclear Information System (INIS)

Wang Wenli; Nehmeh, Sadek A; O'Donoghue, Joseph; Zanzonico, Pat B; Schmidtlein, C Ross; Lee, Nancy Y; Humm, John L; Georgi, Jens-Christoph; Paulus, Timo; Narayanan, Manoj; Bal, Matthieu

2009-01-01

This paper systematically evaluates a pharmacokinetic compartmental model for identifying tumor hypoxia using dynamic positron emission tomography (PET) imaging with 18 F-fluoromisonidazole (FMISO). A generic irreversible one-plasma two-tissue compartmental model was used. A dynamic PET image dataset was simulated with three tumor regions-normoxic, hypoxic and necrotic-embedded in a normal-tissue background, and with an image-based arterial input function. Each voxelized tissue's time activity curve (TAC) was simulated with typical values of kinetic parameters, as deduced from FMISO-PET data from nine head-and-neck cancer patients. The dynamic dataset was first produced without any statistical noise to ensure that correct kinetic parameters were reproducible. Next, to investigate the stability of kinetic parameter estimation in the presence of noise, 1000 noisy samples of the dynamic dataset were generated, from which 1000 noisy estimates of kinetic parameters were calculated and used to estimate the sample mean and covariance matrix. It is found that a more peaked input function gave less variation in various kinetic parameters, and the variation of kinetic parameters could also be reduced by two region-of-interest averaging techniques. To further investigate how bias in the arterial input function affected the kinetic parameter estimation, a shift error was introduced in the peak amplitude and peak location of the input TAC, and the bias of various kinetic parameters calculated. In summary, mathematical phantom studies have been used to determine the statistical accuracy and precision of model-based kinetic analysis, which helps to validate this analysis and provides guidance in planning clinical dynamic FMISO-PET studies.

Noise properties of CT images reconstructed by use of constrained total-variation, data-discrepancy minimization

DEFF Research Database (Denmark)

Rose, Sean; Andersen, Martin S.; Sidky, Emil Y.

2015-01-01

Purpose: The authors develop and investigate iterative image reconstruction algorithms based on data-discrepancy minimization with a total-variation (TV) constraint. The various algorithms are derived with different data-discrepancy measures reflecting the maximum likelihood (ML) principle......: An incremental algorithm framework is developed for this purpose. The instances of the incremental algorithms are derived for solving optimization problems including a data fidelity objective function combined with a constraint on the image TV. For the data fidelity term the authors, compare application....... Simulations demonstrate the iterative algorithms and the resulting image statistical properties for low-dose CT data acquired with sparse projection view angle sampling. Of particular interest is to quantify improvement of image statistical properties by use of the ML data fidelity term. Methods...

Automatic extraction of soft tissues from 3D MRI head images using model driven analysis

International Nuclear Information System (INIS)

Jiang, Hao; Yamamoto, Shinji; Imao, Masanao.

1995-01-01

This paper presents an automatic extraction system (called TOPS-3D : Top Down Parallel Pattern Recognition System for 3D Images) of soft tissues from 3D MRI head images by using model driven analysis algorithm. As the construction of system TOPS we developed, two concepts have been considered in the design of system TOPS-3D. One is the system having a hierarchical structure of reasoning using model information in higher level, and the other is a parallel image processing structure used to extract plural candidate regions for a destination entity. The new points of system TOPS-3D are as follows. (1) The TOPS-3D is a three-dimensional image analysis system including 3D model construction and 3D image processing techniques. (2) A technique is proposed to increase connectivity between knowledge processing in higher level and image processing in lower level. The technique is realized by applying opening operation of mathematical morphology, in which a structural model function defined in higher level by knowledge representation is immediately used to the filter function of opening operation as image processing in lower level. The system TOPS-3D applied to 3D MRI head images consists of three levels. First and second levels are reasoning part, and third level is image processing part. In experiments, we applied 5 samples of 3D MRI head images with size 128 x 128 x 128 pixels to the system TOPS-3D to extract the regions of soft tissues such as cerebrum, cerebellum and brain stem. From the experimental results, the system is robust for variation of input data by using model information, and the position and shape of soft tissues are extracted corresponding to anatomical structure. (author)

Variational approach to chiral quark models

Energy Technology Data Exchange (ETDEWEB)

Futami, Yasuhiko; Odajima, Yasuhiko; Suzuki, Akira

1987-03-01

A variational approach is applied to a chiral quark model to test the validity of the perturbative treatment of the pion-quark interaction based on the chiral symmetry principle. It is indispensably related to the chiral symmetry breaking radius if the pion-quark interaction can be regarded as a perturbation.

Variational Methods for Discontinuous Structures : Applications to Image Segmentation, Continuum Mechanics

CERN Document Server

Tomarelli, Franco

1996-01-01

In recent years many researchers in material science have focused their attention on the study of composite materials, equilibrium of crystals and crack distribution in continua subject to loads. At the same time several new issues in computer vision and image processing have been studied in depth. The understanding of many of these problems has made significant progress thanks to new methods developed in calculus of variations, geometric measure theory and partial differential equations. In particular, new technical tools have been introduced and successfully applied. For example, in order to describe the geometrical complexity of unknown patterns, a new class of problems in calculus of variations has been introduced together with a suitable functional setting: the free-discontinuity problems and the special BV and BH functions. The conference held at Villa Olmo on Lake Como in September 1994 spawned successful discussion of these topics among mathematicians, experts in computer science and material scientis...

Reprint of Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method

Science.gov (United States)

D'Ambra, Pasqua; Tartaglione, Gaetano

2015-04-01

Image segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology.

Cahn–Hilliard Inpainting and a Generalization for Grayvalue Images

KAUST Repository

Burger, Martin; He, Lin; Schö nlieb, Carola-Bibiane

2009-01-01

The Cahn–Hilliard equation is a nonlinear fourth order diffusion equation originating in material science for modeling phase separation and phase coarsening in binary alloys. The inpainting of binary images using the Cahn–Hilliard equation is a new approach in image processing. In this paper we discuss the stationary state of the proposed model and introduce a generalization for grayvalue images of bounded variation. This is realized by using subgradients of the total variation functional within the flow, which leads to structure inpainting with smooth curvature of level sets.

Accelerated gradient methods for total-variation-based CT image reconstruction

Energy Technology Data Exchange (ETDEWEB)

Joergensen, Jakob H.; Hansen, Per Christian [Technical Univ. of Denmark, Lyngby (Denmark). Dept. of Informatics and Mathematical Modeling; Jensen, Tobias L.; Jensen, Soeren H. [Aalborg Univ. (Denmark). Dept. of Electronic Systems; Sidky, Emil Y.; Pan, Xiaochuan [Chicago Univ., Chicago, IL (United States). Dept. of Radiology

2011-07-01

Total-variation (TV)-based CT image reconstruction has shown experimentally to be capable of producing accurate reconstructions from sparse-view data. In particular TV-based reconstruction is well suited for images with piecewise nearly constant regions. Computationally, however, TV-based reconstruction is demanding, especially for 3D imaging, and the reconstruction from clinical data sets is far from being close to real-time. This is undesirable from a clinical perspective, and thus there is an incentive to accelerate the solution of the underlying optimization problem. The TV reconstruction can in principle be found by any optimization method, but in practice the large scale of the systems arising in CT image reconstruction preclude the use of memory-intensive methods such as Newton's method. The simple gradient method has much lower memory requirements, but exhibits prohibitively slow convergence. In the present work we address the question of how to reduce the number of gradient method iterations needed to achieve a high-accuracy TV reconstruction. We consider the use of two accelerated gradient-based methods, GPBB and UPN, to solve the 3D-TV minimization problem in CT image reconstruction. The former incorporates several heuristics from the optimization literature such as Barzilai-Borwein (BB) step size selection and nonmonotone line search. The latter uses a cleverly chosen sequence of auxiliary points to achieve a better convergence rate. The methods are memory efficient and equipped with a stopping criterion to ensure that the TV reconstruction has indeed been found. An implementation of the methods (in C with interface to Matlab) is available for download from http://www2.imm.dtu.dk/~pch/TVReg/. We compare the proposed methods with the standard gradient method, applied to a 3D test problem with synthetic few-view data. We find experimentally that for realistic parameters the proposed methods significantly outperform the standard gradient method. (orig.)

Omics approaches to individual variation: modeling networks and the virtual patient.

Science.gov (United States)

Lehrach, Hans

2016-09-01

Every human is unique. We differ in our genomes, environment, behavior, disease history, and past and current medical treatment-a complex catalog of differences that often leads to variations in the way each of us responds to a particular therapy. We argue here that true personalization of drug therapies will rely on "virtual patient" models based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. The models will be based, wherever possible, on the molecular mechanisms of disease processes and drug action but can also expand to hybrid models including statistics/machine learning/artificial intelligence-based elements trained on available data to address therapeutic areas or therapies for which insufficient information on mechanisms is available. Depending on the disease, its mechanisms, and the therapy, virtual patient models can be implemented at a fairly high level of abstraction, with molecular models representing cells, cell types, or organs relevant to the clinical question, interacting not only with each other but also the environment. In the future, "virtual patient/in-silico self" models may not only become a central element of our health care system, reducing otherwise unavoidable mistakes and unnecessary costs, but also act as "guardian angels" accompanying us through life to protect us against dangers and to help us to deal intelligently with our own health and wellness.

Higher order total variation regularization for EIT reconstruction.

Science.gov (United States)

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Zhang, Fan; Mueller-Lisse, Ullrich; Moeller, Knut

2018-01-08

Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.

Computer model for harmonic ultrasound imaging.

Science.gov (United States)

Li, Y; Zagzebski, J A

2000-01-01

Harmonic ultrasound imaging has received great attention from ultrasound scanner manufacturers and researchers. In this paper, we present a computer model that can generate realistic harmonic images. In this model, the incident ultrasound is modeled after the "KZK" equation, and the echo signal is modeled using linear propagation theory because the echo signal is much weaker than the incident pulse. Both time domain and frequency domain numerical solutions to the "KZK" equation were studied. Realistic harmonic images of spherical lesion phantoms were generated for scans by a circular transducer. This model can be a very useful tool for studying the harmonic buildup and dissipation processes in a nonlinear medium, and it can be used to investigate a wide variety of topics related to B-mode harmonic imaging.

Introductory biology students' conceptual models and explanations of the origin of variation.

Science.gov (United States)

Speth, Elena Bray; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

2014-01-01

Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess understanding of the origin of variation. By midterm, only a small percentage of students articulated complete and accurate representations of the origin of variation in their models. Targeted feedback was offered through activities requiring students to critically evaluate peers' models. At semester's end, a substantial proportion of students significantly improved their representation of how variation arises (though one-third still did not include mutation in their models). Students' written explanations of the origin of variation were mostly consistent with their models, although less effective than models in conveying mechanistic reasoning. This study contributes evidence that articulating the genetic origin of variation is particularly challenging for learners and may require multiple cycles of instruction, assessment, and feedback. To support meaningful learning of the origin of variation, we advocate instruction that explicitly integrates multiple scales of biological organization, assessment that promotes and reveals mechanistic and causal reasoning, and practice with explanatory models with formative feedback. © 2014 E. Bray Speth et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

3D Face Apperance Model

DEFF Research Database (Denmark)

Lading, Brian; Larsen, Rasmus; Astrom, K

2006-01-01

We build a 3D face shape model, including inter- and intra-shape variations, derive the analytical Jacobian of its resulting 2D rendered image, and show example of its fitting performance with light, pose, id, expression and texture variations......We build a 3D face shape model, including inter- and intra-shape variations, derive the analytical Jacobian of its resulting 2D rendered image, and show example of its fitting performance with light, pose, id, expression and texture variations...

Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization

International Nuclear Information System (INIS)

Sidky, Emil Y; Pan Xiaochuan

2008-01-01

An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories

Quantitative Assessment of Variational Surface Reconstruction from Sparse Point Clouds in Freehand 3D Ultrasound Imaging during Image-Guided Tumor Ablation

Directory of Open Access Journals (Sweden)

Shuangcheng Deng

2016-04-01

Full Text Available Surface reconstruction for freehand 3D ultrasound is used to provide 3D visualization of a VOI (volume of interest during image-guided tumor ablation surgery. This is a challenge because the recorded 2D B-scans are not only sparse but also non-parallel. To solve this issue, we established a framework to reconstruct the surface of freehand 3D ultrasound imaging in 2011. The key technique for surface reconstruction in that framework is based on variational interpolation presented by Greg Turk for shape transformation and is named Variational Surface Reconstruction (VSR. The main goal of this paper is to evaluate the quality of surface reconstructions, especially when the input data are extremely sparse point clouds from freehand 3D ultrasound imaging, using four methods: Ball Pivoting, Power Crust, Poisson, and VSR. Four experiments are conducted, and quantitative metrics, such as the Hausdorff distance, are introduced for quantitative assessment. The experiment results show that the performance of the proposed VSR method is the best of the four methods at reconstructing surface from sparse data. The VSR method can produce a close approximation to the original surface from as few as two contours, whereas the other three methods fail to do so. The experiment results also illustrate that the reproducibility of the VSR method is the best of the four methods.

Variational approach for restoring blurred images with cauchy noise

DEFF Research Database (Denmark)

Sciacchitano, Federica; Dong, Yiqiu; Zeng, Tieyong

2015-01-01

model, we add a quadratic penalty term, which guarantees the uniqueness of the solution. Due to the convexity of our model, the primal dual algorithm is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for simultaneously deblurring...... and denoising images corrupted by Cauchy noise. Comparison with other existing and well-known methods is provided as well....

Implicit Active Contours Driven by Local and Global Image Fitting Energy for Image Segmentation and Target Localization

Directory of Open Access Journals (Sweden)

Xiaosheng Yu

2013-01-01

Full Text Available We propose a novel active contour model in a variational level set formulation for image segmentation and target localization. We combine a local image fitting term and a global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity with the contour starting anywhere in the image. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. We validated its effectiveness in numerous synthetic images and real images, and the promising experimental results show its advantages in terms of accuracy, efficiency, and robustness.

Modeling stimulus variation in three common implicit attitude tasks.

Science.gov (United States)

Wolsiefer, Katie; Westfall, Jacob; Judd, Charles M

2017-08-01

We explored the consequences of ignoring the sampling variation due to stimuli in the domain of implicit attitudes. A large literature in psycholinguistics has examined the statistical treatment of random stimulus materials, but the recommendations from this literature have not been applied to the social psychological literature on implicit attitudes. This is partly because of inherent complications in applying crossed random-effect models to some of the most common implicit attitude tasks, and partly because no work to date has demonstrated that random stimulus variation is in fact consequential in implicit attitude measurement. We addressed this problem by laying out statistically appropriate and practically feasible crossed random-effect models for three of the most commonly used implicit attitude measures-the Implicit Association Test, affect misattribution procedure, and evaluative priming task-and then applying these models to large datasets (average N = 3,206) that assess participants' implicit attitudes toward race, politics, and self-esteem. We showed that the test statistics from the traditional analyses are substantially (about 60 %) inflated relative to the more-appropriate analyses that incorporate stimulus variation. Because all three tasks used the same stimulus words and faces, we could also meaningfully compare the relative contributions of stimulus variation across the tasks. In an appendix, we give syntax in R, SAS, and SPSS for fitting the recommended crossed random-effects models to data from all three tasks, as well as instructions on how to structure the data file.

In Vivo MR Imaging of Magnetically Labeled Mesenchymal Stem Cells in a Rat Model of Renal Ischemia

Energy Technology Data Exchange (ETDEWEB)

Jung, Sung Il [Konkuk University Medical Center, Seoul (Korea, Republic of); Kim, Seung Hyup [Seoul National University Medical Research Center, Seoul (Korea, Republic of); Kim, Hyo Cheol; Chung, Se Young; Moon, Woo Kyung; Kim, Hoe Suk [Seoul National University Hospital, Seoul (Korea, Republic of); Choi, Jong Sun [Dongguk University International Hospital, Goyang (Korea, Republic of); Moon, Min Hoan [Cheil General Hospital and Women' s Healthcare Center, Seoul (Korea, Republic of); Son, Kyu Ri; Sung, Chang Kyu [Seoul National University Boramae Hospital, Seoul (Korea, Republic of)

2009-06-15

This study was designed to evaluate in vivo MR imaging for the depiction of intraarterially injected superparamagnetic iron oxide (SPIO)-labeled mesenchymal stem cells (MSCs) in an experimental rat model of renal ischemia. Left renal ischemia was induced in 12 male Sprague- Dawley rats by use of the catheter lodging method. In vivo MR signal intensity variations depicted on T2*-weighted sequences were evaluated in both the left and right kidneys prior to injection (n = 2), two hours (n = 4), 15 hours (n = 2), 30 hours (n = 2) and 72 hours (n = 2) after injection of SPIO-labeled MSCs in both kidneys. Signal intensity variations were correlated with the number of Prussian blue stain-positive cells as visualized in histological specimens. In an in vivo study, it was determined that there was a significant difference in signal intensity variation for both the left and right cortex (40.8 {+-} 4.12 and 26.4 {+-} 7.92, respectively) and for both the left and right medulla (23.2 {+-} 3.32 and 15.2 {+-} 3.31, respectively) until two hours after injection (p < 0.05). In addition, signal intensity variation in the left renal cortex was well correlated with the number of Prussian blue stain-positive cells per high power field (r = 0.98, p < 0.05). Intraarterial injected SPIO-labeled MSCs in an experimental rat model of renal ischemia can be detected with the use of in vivo MR imaging immediately after injection.

Properties of Brownian Image Models in Scale-Space

DEFF Research Database (Denmark)

Pedersen, Kim Steenstrup

2003-01-01

Brownian images) will be discussed in relation to linear scale-space theory, and it will be shown empirically that the second order statistics of natural images mapped into jet space may, within some scale interval, be modeled by the Brownian image model. This is consistent with the 1/f 2 power spectrum...... law that apparently governs natural images. Furthermore, the distribution of Brownian images mapped into jet space is Gaussian and an analytical expression can be derived for the covariance matrix of Brownian images in jet space. This matrix is also a good approximation of the covariance matrix......In this paper it is argued that the Brownian image model is the least committed, scale invariant, statistical image model which describes the second order statistics of natural images. Various properties of three different types of Gaussian image models (white noise, Brownian and fractional...

Compensation of PVT Variations in ToF Imagers with In-Pixel TDC.

Science.gov (United States)

Vornicu, Ion; Carmona-Galán, Ricardo; Rodríguez-Vázquez, Ángel

2017-05-09

The design of a direct time-of-flight complementary metal-oxide-semiconductor (CMOS) image sensor (dToF-CIS) based on a single-photon avalanche-diode (SPAD) array with an in-pixel time-to-digital converter (TDC) must contemplate system-level aspects that affect its overall performance. This paper provides a detailed analysis of the impact of process parameters, voltage supply, and temperature (PVT) variations on the time bin of the TDC array. Moreover, the design and characterization of a global compensation loop is presented. It is based on a phase locked loop (PLL) that is integrated on-chip. The main building block of the PLL is a voltage-controlled ring-oscillator (VCRO) that is identical to the ones employed for the in-pixel TDCs. The reference voltage that drives the master VCRO is distributed to the voltage control inputs of the slave VCROs such that their multiphase outputs become invariant to PVT changes. These outputs act as time interpolators for the TDCs. Therefore the compensation scheme prevents the time bin of the TDCs from drifting over time due to the aforementioned factors. Moreover, the same scheme is used to program different time resolutions of the direct time-of-flight (ToF) imager aimed at 3D ranging or depth map imaging. Experimental results that validate the analysis are provided as well. The compensation loop proves to be remarkably effective. The spreading of the TDCs time bin is lowered from: (i) 20% down to 2.4% while the temperature ranges from 0 °C to 100 °C; (ii) 27% down to 0.27%, when the voltage supply changes within ±10% of the nominal value; (iii) 5.2 ps to 2 ps standard deviation over 30 sample chips, due to process parameters' variation.

Biomedical Imaging and Computational Modeling in Biomechanics

CERN Document Server

Iacoviello, Daniela

2013-01-01

This book collects the state-of-art and new trends in image analysis and biomechanics. It covers a wide field of scientific and cultural topics, ranging from remodeling of bone tissue under the mechanical stimulus up to optimizing the performance of sports equipment, through the patient-specific modeling in orthopedics, microtomography and its application in oral and implant research, computational modeling in the field of hip prostheses, image based model development and analysis of the human knee joint, kinematics of the hip joint, micro-scale analysis of compositional and mechanical properties of dentin, automated techniques for cervical cell image analysis, and iomedical imaging and computational modeling in cardiovascular disease.   The book will be of interest to researchers, Ph.D students, and graduate students with multidisciplinary interests related to image analysis and understanding, medical imaging, biomechanics, simulation and modeling, experimental analysis.

4D RECONSTRUCTIONS FROM LOW-COUNT SPECT DATA USING DEFORMABLE MODELS WITH SMOOTH INTERIOR INTENSITY VARIATIONS

International Nuclear Information System (INIS)

Cunningham, G. S.; Lehovich, A.

2000-01-01

The Bayes Inference Engine (BIE) has been used to perform a 4D reconstruction of a first-pass radiotracer bolus distribution inside a CardioWest Total Artificial Heart, imaged with the University of Arizona's FastSPECT system. The BIE estimates parameter values that define the 3D model of the radiotracer distribution at each of 41 times spanning about two seconds. The 3D models have two components: a closed surface, composed of hi-quadratic Bezier triangular surface patches, that defines the interface between the part of the blood pool that contains radiotracer and the part that contains no radiotracer, and smooth voxel-to-voxel variations in intensity within the closed surface. Ideally, the surface estimates the ventricular wall location where the bolus is infused throughout the part of the blood pool contained by the right ventricle. The voxel-to-voxel variations are needed to model an inhomogeneously-mixed bolus. Maximum a posterior (MAP) estimates of the Bezier control points and voxel values are obtained for each time frame. We show new reconstructions using the Bezier surface models, and discuss estimates of ventricular volume as a function of time, ejection fraction, and wall motion. The computation time for our reconstruction process, which directly estimates complex 3D model parameters from the raw data, is performed in a time that is competitive with more traditional voxel-based methods (ML-EM, e.g.)

Automated quantification and sizing of unbranched filamentous cyanobacteria by model based object oriented image analysis

OpenAIRE

Zeder, M; Van den Wyngaert, S; Köster, O; Felder, K M; Pernthaler, J

2010-01-01

Quantification and sizing of filamentous cyanobacteria in environmental samples or cultures are time-consuming and are often performed by using manual or semiautomated microscopic analysis. Automation of conventional image analysis is difficult because filaments may exhibit great variations in length and patchy autofluorescence. Moreover, individual filaments frequently cross each other in microscopic preparations, as deduced by modeling. This paper describes a novel approach based on object-...

Storm surge model based on variational data assimilation method

Directory of Open Access Journals (Sweden)

Shi-li Huang

2010-06-01

Full Text Available By combining computation and observation information, the variational data assimilation method has the ability to eliminate errors caused by the uncertainty of parameters in practical forecasting. It was applied to a storm surge model based on unstructured grids with high spatial resolution meant for improving the forecasting accuracy of the storm surge. By controlling the wind stress drag coefficient, the variation-based model was developed and validated through data assimilation tests in an actual storm surge induced by a typhoon. In the data assimilation tests, the model accurately identified the wind stress drag coefficient and obtained results close to the true state. Then, the actual storm surge induced by Typhoon 0515 was forecast by the developed model, and the results demonstrate its efficiency in practical application.

Biophysical modelling of intra-ring variations in tracheid features and wood density of Pinus pinaster trees exposed to seasonal droughts.

Science.gov (United States)

Wilkinson, Sarah; Ogée, Jérôme; Domec, Jean-Christophe; Rayment, Mark; Wingate, Lisa

2015-03-01

Process-based models that link seasonally varying environmental signals to morphological features within tree rings are essential tools to predict tree growth response and commercially important wood quality traits under future climate scenarios. This study evaluated model portrayal of radial growth and wood anatomy observations within a mature maritime pine (Pinus pinaster (L.) Aït.) stand exposed to seasonal droughts. Intra-annual variations in tracheid anatomy and wood density were identified through image analysis and X-ray densitometry on stem cores covering the growth period 1999-2010. A cambial growth model was integrated with modelled plant water status and sugar availability from the soil-plant-atmosphere transfer model MuSICA to generate estimates of cell number, cell volume, cell mass and wood density on a weekly time step. The model successfully predicted inter-annual variations in cell number, ring width and maximum wood density. The model was also able to predict the occurrence of special anatomical features such as intra-annual density fluctuations (IADFs) in growth rings. Since cell wall thickness remained surprisingly constant within and between growth rings, variations in wood density were primarily the result of variations in lumen diameter, both in the model and anatomical data. In the model, changes in plant water status were identified as the main driver of the IADFs through a direct effect on cell volume. The anatomy data also revealed that a trade-off existed between hydraulic safety and hydraulic efficiency. Although a simplified description of cambial physiology is presented, this integrated modelling approach shows potential value for identifying universal patterns of tree-ring growth and anatomical features over a broad climatic gradient. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Parameter estimation for a cohesive sediment transport model by assimilating satellite observations in the Hangzhou Bay: Temporal variations and spatial distributions

Science.gov (United States)

Wang, Daosheng; Zhang, Jicai; He, Xianqiang; Chu, Dongdong; Lv, Xianqing; Wang, Ya Ping; Yang, Yang; Fan, Daidu; Gao, Shu

2018-01-01

Model parameters in the suspended cohesive sediment transport models are critical for the accurate simulation of suspended sediment concentrations (SSCs). Difficulties in estimating the model parameters still prevent numerical modeling of the sediment transport from achieving a high level of predictability. Based on a three-dimensional cohesive sediment transport model and its adjoint model, the satellite remote sensing data of SSCs during both spring tide and neap tide, retrieved from Geostationary Ocean Color Imager (GOCI), are assimilated to synchronously estimate four spatially and temporally varying parameters in the Hangzhou Bay in China, including settling velocity, resuspension rate, inflow open boundary conditions and initial conditions. After data assimilation, the model performance is significantly improved. Through several sensitivity experiments, the spatial and temporal variation tendencies of the estimated model parameters are verified to be robust and not affected by model settings. The pattern for the variations of the estimated parameters is analyzed and summarized. The temporal variations and spatial distributions of the estimated settling velocity are negatively correlated with current speed, which can be explained using the combination of flocculation process and Stokes' law. The temporal variations and spatial distributions of the estimated resuspension rate are also negatively correlated with current speed, which are related to the grain size of the seabed sediments under different current velocities. Besides, the estimated inflow open boundary conditions reach the local maximum values near the low water slack conditions and the estimated initial conditions are negatively correlated with water depth, which is consistent with the general understanding. The relationships between the estimated parameters and the hydrodynamic fields can be suggestive for improving the parameterization in cohesive sediment transport models.

Image-optimized Coronal Magnetic Field Models

Energy Technology Data Exchange (ETDEWEB)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M., E-mail: shaela.i.jones-mecholsky@nasa.gov, E-mail: shaela.i.jonesmecholsky@nasa.gov [NASA Goddard Space Flight Center, Code 670, Greenbelt, MD 20771 (United States)

2017-08-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.

Image-Optimized Coronal Magnetic Field Models

Science.gov (United States)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-01-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work we presented early tests of the method which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane, and the effect on the outcome of the optimization of errors in localization of constraints. We find that substantial improvement in the model field can be achieved with this type of constraints, even when magnetic features in the images are located outside of the image plane.

Influence of ASIR (Adaptative Statistical Iterative Reconstruction) variation in the image noise of computerized tomography for high voltage

International Nuclear Information System (INIS)

Mendes, L.M.M.; Pereira, W.B.R.; Vieira, J.G.; Lamounier, C.S.; Gonçalves, D.A.; Carvalho, G.N.P.; Santana, P.C.; Oliveira, P.M.C.; Reis, L.P.

2017-01-01

Computed tomography had great advances in the equipment used in the diagnostic practice, directly influencing the levels of radiation for the patient. It is essential to optimize techniques that must be employed to comply with the ALARA (As Low As Reasonably Achievable) principle of radioprotection. The relationship of ASIR (Adaptive Statistical Iterative Reconstruction) with image noise was studied. Central images of a homogeneous water simulator were obtained in a 20 mm scan using a 64-channel Lightspeed VCT tomograph of General Electric in helical acquisitions with a rotation time of 0.5 seconds, Pitch 0.984: 1, and thickness of cut 0.625 mm. All these constant parameters varying the voltage in two distinct values: 120 and 140 kV with use of the automatic current by the CAE (Automatic Exposure Control), ranging from 50 to 675 mA (120 kV) and from 50 to 610 mA (140kV), minimum and maximum values, respectively allowed for each voltage. Image noise was determined through ImageJ free software. The analysis of the obtained data compared the percentage variation of the noise in the image based on the ASIR value of 10%, concluding that there is a variation of approximately 50% when compared to the values of ASIR (100%) in both tensions. Dose evaluation is required in future studies to better utilize the relationship between dose and image quality

A variational approach to the Gross-Neveu model

International Nuclear Information System (INIS)

Mishra, H.; Misra, P.; Mishra, A.

1988-01-01

The authors solve the instability of perturbative vacuum of Gross-Neveu model. They use a variational method. The analysis is nonperturbative as it uses only equal time commmutator/anticommutator algebra

National variation in preoperative imaging, carotid duplex ultrasound criteria, and threshold for surgery for asymptomatic carotid artery stenosis.

Science.gov (United States)

Arous, Edward J; Simons, Jessica P; Flahive, Julie M; Beck, Adam W; Stone, David H; Hoel, Andrew W; Messina, Louis M; Schanzer, Andres

2015-10-01

Carotid endarterectomy (CEA) for asymptomatic carotid artery stenosis is among the most common procedures performed in the United States. However, consensus is lacking regarding optimal preoperative imaging, carotid duplex ultrasound criteria, and ultimately, the threshold for surgery. We sought to characterize national variation in preoperative imaging, carotid duplex ultrasound criteria, and threshold for surgery for asymptomatic CEA. The Society for Vascular Surgery Vascular Quality Initiative (VQI) database was used to identify all CEA procedures performed for asymptomatic carotid artery stenosis between 2003 and 2014. VQI currently captures 100% of CEA procedures performed at >300 centers by >2000 physicians nationwide. Three analyses were performed to quantify the variation in (1) preoperative imaging, (2) carotid duplex ultrasound criteria, and (3) threshold for surgery. Of 35,695 CEA procedures in 33,488 patients, the study cohort was limited to 19,610 CEA procedures (55%) performed for asymptomatic disease. The preoperative imaging modality used before CEA varied widely, with 57% of patients receiving a single preoperative imaging study (duplex ultrasound imaging, 46%; computed tomography angiography, 7.5%; magnetic resonance angiography, 2.0%; cerebral angiography, 1.3%) and 43% of patients receiving multiple preoperative imaging studies. Of the 16,452 asymptomatic patients (89%) who underwent preoperative duplex ultrasound imaging, there was significant variability between centers in the degree of stenosis (50%-69%, 70%-79%, 80%-99%) designated for a given peak systolic velocity, end diastolic velocity, and internal carotid artery-to-common carotid artery ratio. Although 68% of CEA procedures in asymptomatic patients were performed for an 80% to 99% stenosis, 26% were performed for a 70% to 79% stenosis, and 4.1% were performed for a 50% to 69% stenosis. At the surgeon level, the range in the percentage of CEA procedures performed for a duplex ultrasound

Stigma models: Testing hypotheses of how images of Nevada are acquired and values are attached to them

Energy Technology Data Exchange (ETDEWEB)

Jenkins-Smith, H.C. [New Mexico Univ., Albuquerque, NM (United States)

1994-12-01

This report analyzes data from surveys on the effects that images associated with nuclear power and waste (i.e., nuclear images) have on people`s preference to vacation in Nevada. The analysis was stimulated by a model of imagery and stigma which assumes that information about a potentially hazardous facility generates signals that elicit negative images about the place in which it is located. Individuals give these images negative values (valences) that lessen their desire to vacation, relocate, or retire in that place. The model has been used to argue that the proposed Yucca Mountain high-level nuclear waste repository could elicit images of nuclear waste that would stigmatize Nevada and thus impose substantial economic losses there. This report proposes a revised model that assumes that the acquisition and valuation of images depend on individuals` ideological and cultural predispositions and that the ways in which new images will affect their preferences and behavior partly depend on these predispositions. The report tests these hypotheses: (1) individuals with distinct cultural and ideological predispositions have different propensities for acquiring nuclear images, (2) these people attach different valences to these images, (3) the variations in these valences are important, and (4) the valences of the different categories of images within an individual`s image sets for a place correlate very well. The analysis largely confirms these hypotheses, indicating that the stigma model should be revised to (1) consider the relevant ideological and cultural predispositions of the people who will potentially acquire and attach value to the image, (2) specify the kinds of images that previously attracted people to the host state, and (3) consider interactions between the old and potential new images of the place. 37 refs., 18 figs., 17 tabs.

Stigma models: Testing hypotheses of how images of Nevada are acquired and values are attached to them

International Nuclear Information System (INIS)

Jenkins-Smith, H.C.

1994-12-01

This report analyzes data from surveys on the effects that images associated with nuclear power and waste (i.e., nuclear images) have on people's preference to vacation in Nevada. The analysis was stimulated by a model of imagery and stigma which assumes that information about a potentially hazardous facility generates signals that elicit negative images about the place in which it is located. Individuals give these images negative values (valences) that lessen their desire to vacation, relocate, or retire in that place. The model has been used to argue that the proposed Yucca Mountain high-level nuclear waste repository could elicit images of nuclear waste that would stigmatize Nevada and thus impose substantial economic losses there. This report proposes a revised model that assumes that the acquisition and valuation of images depend on individuals' ideological and cultural predispositions and that the ways in which new images will affect their preferences and behavior partly depend on these predispositions. The report tests these hypotheses: (1) individuals with distinct cultural and ideological predispositions have different propensities for acquiring nuclear images, (2) these people attach different valences to these images, (3) the variations in these valences are important, and (4) the valences of the different categories of images within an individual's image sets for a place correlate very well. The analysis largely confirms these hypotheses, indicating that the stigma model should be revised to (1) consider the relevant ideological and cultural predispositions of the people who will potentially acquire and attach value to the image, (2) specify the kinds of images that previously attracted people to the host state, and (3) consider interactions between the old and potential new images of the place. 37 refs., 18 figs., 17 tabs

Remotely monitoring evaporation rate and soil water status using thermal imaging and "three-temperatures model (3T Model)" under field-scale conditions.

Science.gov (United States)

Qiu, Guo Yu; Zhao, Ming

2010-03-01

Remote monitoring of soil evaporation and soil water status is necessary for water resource and environment management. Ground based remote sensing can be the bridge between satellite remote sensing and ground-based point measurement. The primary object of this study is to provide an algorithm to estimate evaporation and soil water status by remote sensing and to verify its accuracy. Observations were carried out in a flat field with varied soil water content. High-resolution thermal images were taken with a thermal camera; soil evaporation was measured with a weighing lysimeter; weather data were recorded at a nearby meteorological station. Based on the thermal imaging and the three-temperatures model (3T model), we developed an algorithm to estimate soil evaporation and soil water status. The required parameters of the proposed method were soil surface temperature, air temperature, and solar radiation. By using the proposed method, daily variation in soil evaporation was estimated. Meanwhile, soil water status was remotely monitored by using the soil evaporation transfer coefficient. Results showed that the daily variation trends of measured and estimated evaporation agreed with each other, with a regression line of y = 0.92x and coefficient of determination R(2) = 0.69. The simplicity of the proposed method makes the 3T model a potentially valuable tool for remote sensing.

A generalized logarithmic image processing model based on the gigavision sensor model.

Science.gov (United States)

Deng, Guang

2012-03-01

The logarithmic image processing (LIP) model is a mathematical theory providing generalized linear operations for image processing. The gigavision sensor (GVS) is a new imaging device that can be described by a statistical model. In this paper, by studying these two seemingly unrelated models, we develop a generalized LIP (GLIP) model. With the LIP model being its special case, the GLIP model not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily related to the GVS. A new parametric LIP model is also developed. To illustrate the application of the new scalar multiplication operation, we propose an energy-preserving algorithm for tone mapping, which is a necessary step in image dehazing. By comparing with results using two state-of-the-art algorithms, we show that the new scalar multiplication operation is an effective tool for tone mapping.

Imaging with Kantorovich--Rubinstein Discrepancy

KAUST Repository

Lellmann, Jan

2014-01-01

© 2014 Society for Industrial and Applied Mathematics. We propose the use of the Kantorovich-Rubinstein norm from optimal transport in imaging problems. In particular, we discuss a variational regularization model endowed with a Kantorovich- Rubinstein discrepancy term and total variation regularization in the context of image denoising and cartoon-texture decomposition. We point out connections of this approach to several other recently proposed methods such as total generalized variation and norms capturing oscillating patterns. We also show that the respective optimization problem can be turned into a convex-concave saddle point problem with simple constraints and hence can be solved by standard tools. Numerical examples exhibit interesting features and favorable performance for denoising and cartoon-texture decomposition.

Segmentation of Concealed Objects in Passive Millimeter-Wave Images Based on the Gaussian Mixture Model

Science.gov (United States)

Yu, Wangyang; Chen, Xiangguang; Wu, Lei

2015-04-01

Passive millimeter wave (PMMW) imaging has become one of the most effective means to detect the objects concealed under clothing. Due to the limitations of the available hardware and the inherent physical properties of PMMW imaging systems, images often exhibit poor contrast and low signal-to-noise ratios. Thus, it is difficult to achieve ideal results by using a general segmentation algorithm. In this paper, an advanced Gaussian Mixture Model (GMM) algorithm for the segmentation of concealed objects in PMMW images is presented. Our work is concerned with the fact that the GMM is a parametric statistical model, which is often used to characterize the statistical behavior of images. Our approach is three-fold: First, we remove the noise from the image using both a notch reject filter and a total variation filter. Next, we use an adaptive parameter initialization GMM algorithm (APIGMM) for simulating the histogram of images. The APIGMM provides an initial number of Gaussian components and start with more appropriate parameter. Bayesian decision is employed to separate the pixels of concealed objects from other areas. At last, the confidence interval (CI) method, alongside local gradient information, is used to extract the concealed objects. The proposed hybrid segmentation approach detects the concealed objects more accurately, even compared to two other state-of-the-art segmentation methods.

Decision-case mix model for analyzing variation in cesarean rates.

Science.gov (United States)

Eldenburg, L; Waller, W S

2001-01-01

This article contributes a decision-case mix model for analyzing variation in c-section rates. Like recent contributions to the literature, the model systematically takes into account the effect of case mix. Going beyond past research, the model highlights differences in physician decision making in response to obstetric factors. Distinguishing the effects of physician decision making and case mix is important in understanding why c-section rates vary and in developing programs to effect change in physician behavior. The model was applied to a sample of deliveries at a hospital where physicians exhibited considerable variation in their c-section rates. Comparing groups with a low versus high rate, the authors' general conclusion is that the difference in physician decision tendencies (to perform a c-section), in response to specific obstetric factors, is at least as important as case mix in explaining variation in c-section rates. The exact effects of decision making versus case mix depend on how the model application defines the obstetric condition of interest and on the weighting of deliveries by their estimated "risk of Cesarean." The general conclusion is supported by an additional analysis that uses the model's elements to predict individual physicians' annual c-section rates.

The role of ensemble-based statistics in variational assimilation of cloud-affected observations from infrared imagers

Science.gov (United States)

Hacker, Joshua; Vandenberghe, Francois; Jung, Byoung-Jo; Snyder, Chris

2017-04-01

Effective assimilation of cloud-affected radiance observations from space-borne imagers, with the aim of improving cloud analysis and forecasting, has proven to be difficult. Large observation biases, nonlinear observation operators, and non-Gaussian innovation statistics present many challenges. Ensemble-variational data assimilation (EnVar) systems offer the benefits of flow-dependent background error statistics from an ensemble, and the ability of variational minimization to handle nonlinearity. The specific benefits of ensemble statistics, relative to static background errors more commonly used in variational systems, have not been quantified for the problem of assimilating cloudy radiances. A simple experiment framework is constructed with a regional NWP model and operational variational data assimilation system, to provide the basis understanding the importance of ensemble statistics in cloudy radiance assimilation. Restricting the observations to those corresponding to clouds in the background forecast leads to innovations that are more Gaussian. The number of large innovations is reduced compared to the more general case of all observations, but not eliminated. The Huber norm is investigated to handle the fat tails of the distributions, and allow more observations to be assimilated without the need for strict background checks that eliminate them. Comparing assimilation using only ensemble background error statistics with assimilation using only static background error statistics elucidates the importance of the ensemble statistics. Although the cost functions in both experiments converge to similar values after sufficient outer-loop iterations, the resulting cloud water, ice, and snow content are greater in the ensemble-based analysis. The subsequent forecasts from the ensemble-based analysis also retain more condensed water species, indicating that the local environment is more supportive of clouds. In this presentation we provide details that explain the

Models for Patch-Based Image Restoration

Directory of Open Access Journals (Sweden)

Petrovic Nemanja

2009-01-01

Full Text Available Abstract We present a supervised learning approach for object-category specific restoration, recognition, and segmentation of images which are blurred using an unknown kernel. The novelty of this work is a multilayer graphical model which unifies the low-level vision task of restoration and the high-level vision task of recognition in a cooperative framework. The graphical model is an interconnected two-layer Markov random field. The restoration layer accounts for the compatibility between sharp and blurred images and models the association between adjacent patches in the sharp image. The recognition layer encodes the entity class and its location in the underlying scene. The potentials are represented using nonparametric kernel densities and are learnt from training data. Inference is performed using nonparametric belief propagation. Experiments demonstrate the effectiveness of our model for the restoration and recognition of blurred license plates as well as face images.

Models for Patch-Based Image Restoration

Directory of Open Access Journals (Sweden)

Mithun Das Gupta

2009-01-01

Full Text Available We present a supervised learning approach for object-category specific restoration, recognition, and segmentation of images which are blurred using an unknown kernel. The novelty of this work is a multilayer graphical model which unifies the low-level vision task of restoration and the high-level vision task of recognition in a cooperative framework. The graphical model is an interconnected two-layer Markov random field. The restoration layer accounts for the compatibility between sharp and blurred images and models the association between adjacent patches in the sharp image. The recognition layer encodes the entity class and its location in the underlying scene. The potentials are represented using nonparametric kernel densities and are learnt from training data. Inference is performed using nonparametric belief propagation. Experiments demonstrate the effectiveness of our model for the restoration and recognition of blurred license plates as well as face images.

A Learning State-Space Model for Image Retrieval

Directory of Open Access Journals (Sweden)

Lee Greg C

2007-01-01

Full Text Available This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.

A variational approach to chiral quark models

International Nuclear Information System (INIS)

Futami, Yasuhiko; Odajima, Yasuhiko; Suzuki, Akira.

1987-01-01

A variational approach is applied to a chiral quark model to test the validity of the perturbative treatment of the pion-quark interaction based on the chiral symmetry principle. It is indispensably related to the chiral symmetry breaking radius if the pion-quark interaction can be regarded as a perturbation. (author)

Insights into pre-reversal paleosecular variation from stochastic models

Directory of Open Access Journals (Sweden)

Klaudio ePeqini

2015-09-01

Full Text Available To provide insights on the paleosecular variation of the geomagnetic field and the mechanism of reversals, long time series of the dipolar magnetic moment are generated by two different stochastic models, known as the domino model and the inhomogeneous Lebovitz disk dynamo model, with initial values taken from the from paleomagnetic data. The former model considers mutual interactions of N macrospins embedded in a uniformly rotating medium, where random forcing and dissipation act on each macrospin. With an appropriate set of the model’s parameters values, the series generated by this model have similar statistical behaviour to the time series of the SHA.DIF.14K model. The latter model is an extension of the classical two-disk Rikitake model, considering N dynamo elements with appropriate interactions between them.We varied the parameters set of both models aiming at generating suitable time series with behaviour similar to the long time series of recent secular variation (SV. Such series are then extended to the near future, obtaining reversals in both cases of models. The analysis of the time series generated by simulating the models show that the reversals appears after a persistent period of low intensity geomagnetic field, as it is occurring in the present times.

Evaluation of quantitative imaging methods for organ activity and residence time estimation using a population of phantoms having realistic variations in anatomy and uptake

International Nuclear Information System (INIS)

He Bin; Du Yong; Segars, W. Paul; Wahl, Richard L.; Sgouros, George; Jacene, Heather; Frey, Eric C.

2009-01-01

Estimating organ residence times is an essential part of patient-specific dosimetry for radioimmunotherapy (RIT). Quantitative imaging methods for RIT are often evaluated using a single physical or simulated phantom but are intended to be applied clinically where there is variability in patient anatomy, biodistribution, and biokinetics. To provide a more relevant evaluation, the authors have thus developed a population of phantoms with realistic variations in these factors and applied it to the evaluation of quantitative imaging methods both to find the best method and to demonstrate the effects of these variations. Using whole body scans and SPECT/CT images, organ shapes and time-activity curves of 111In ibritumomab tiuxetan were measured in dosimetrically important organs in seven patients undergoing a high dose therapy regimen. Based on these measurements, we created a 3D NURBS-based cardiac-torso (NCAT)-based phantom population. SPECT and planar data at realistic count levels were then simulated using previously validated Monte Carlo simulation tools. The projections from the population were used to evaluate the accuracy and variation in accuracy of residence time estimation methods that used a time series of SPECT and planar scans. Quantitative SPECT (QSPECT) reconstruction methods were used that compensated for attenuation, scatter, and the collimator-detector response. Planar images were processed with a conventional (CPlanar) method that used geometric mean attenuation and triple-energy window scatter compensation and a quantitative planar (QPlanar) processing method that used model-based compensation for image degrading effects. Residence times were estimated from activity estimates made at each of five time points. The authors also evaluated hybrid methods that used CPlanar or QPlanar time-activity curves rescaled to the activity estimated from a single QSPECT image. The methods were evaluated in terms of mean relative error and standard deviation of the

A medical imaging analysis system for trigger finger using an adaptive texture-based active shape model (ATASM in ultrasound images.

Directory of Open Access Journals (Sweden)

Bo-I Chuang

Full Text Available Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions. The pathology is then determined according to the wavelet and co-occurrence texture features of the segmented tendon area. In the experiments, the segmentation results have fewer errors, with respect to the ground truth, than contours drawn by regular users. The mean values of the absolute segmentation difference of the tendon and synovial sheath are 3.14 and 4.54 pixels, respectively. The average accuracy of pathological determination is 87.14%. The segmentation results are all acceptable in data of both clear and fuzzy boundary cases in 74 images. And the symptom classifications of 42 cases are also a good reference for diagnosis according to the expert clinicians' opinions.

Correlation of breast image alignment using biomechanical modelling

Science.gov (United States)

Lee, Angela; Rajagopal, Vijay; Bier, Peter; Nielsen, Poul M. F.; Nash, Martyn P.

2009-02-01

Breast cancer is one of the most common causes of cancer death among women around the world. Researchers have found that a combination of imaging modalities (such as x-ray mammography, magnetic resonance, and ultrasound) leads to more effective diagnosis and management of breast cancers because each imaging modality displays different information about the breast tissues. In order to aid clinicians in interpreting the breast images from different modalities, we have developed a computational framework for generating individual-specific, 3D, finite element (FE) models of the breast. Medical images are embedded into this model, which is subsequently used to simulate the large deformations that the breasts undergo during different imaging procedures, thus warping the medical images to the deformed views of the breast in the different modalities. In this way, medical images of the breast taken in different geometric configurations (compression, gravity, etc.) can be aligned according to physically feasible transformations. In order to analyse the accuracy of the biomechanical model predictions, squared normalised cross correlation (NCC2) was used to provide both local and global comparisons of the model-warped images with clinical images of the breast subject to different gravity loaded states. The local comparison results were helpful in indicating the areas for improvement in the biomechanical model. To improve the modelling accuracy, we will need to investigate the incorporation of breast tissue heterogeneity into the model and altering the boundary conditions for the breast model. A biomechanical image registration tool of this kind will help radiologists to provide more reliable diagnosis and localisation of breast cancer.

Optimal transport for applied mathematicians calculus of variations, PDEs, and modeling

CERN Document Server

Santambrogio, Filippo

2015-01-01

This monograph presents a rigorous mathematical introduction to optimal transport as a variational problem, its use in modeling various phenomena, and its connections with partial differential equations. Its main goal is to provide the reader with the techniques necessary to understand the current research in optimal transport and the tools which are most useful for its applications. Full proofs are used to illustrate mathematical concepts and each chapter includes a section that discusses applications of optimal transport to various areas, such as economics, finance, potential games, image processing and fluid dynamics. Several topics are covered that have never been previously in books on this subject, such as the Knothe transport, the properties of functionals on measures, the Dacorogna-Moser flow, the formulation through minimal flows with prescribed divergence formulation, the case of the supremal cost, and the most classical numerical methods. Graduate students and researchers in both pure and appl...

Significance of operator variation and the angle of illumination in lineament analysis on synoptic images. [LANDSAT geological investigations

Science.gov (United States)

Siegal, B. S.; Short, N. M.

1977-01-01

The significance of operator variation and the angle of illumination in acquired imagery is analyzed for lineament analysis. Five operators analyzed a LANDSAT image and four photographs of a plastic relief map illuminated at a low angle from varying directions of the Prescott, Arizona region. Significant differences were found in both number and length of the lineaments recognized by the different investigators for the images. The actual coincidence of lineaments recognized by the investigators for the same image is exceptionally low. Even the directional data on lineament orientation is significantly different from operator to operator and from image to image. Cluster analysis of the orientation data displays a clustering by operators rather than by images. It is recommended that extreme caution be taken before attempting to compare different investigators' results in lineament analysis.

Imaging Seismic Source Variations Using Back-Projection Methods at El Tatio Geyser Field, Northern Chile

Science.gov (United States)

Kelly, C. L.; Lawrence, J. F.

2014-12-01

During October 2012, 51 geophones and 6 broadband seismometers were deployed in an ~50x50m region surrounding a periodically erupting columnar geyser in the El Tatio Geyser Field, Chile. The dense array served as the seismic framework for a collaborative project to study the mechanics of complex hydrothermal systems. Contemporaneously, complementary geophysical measurements (including down-hole temperature and pressure, discharge rates, thermal imaging, water chemistry, and video) were also collected. Located on the western flanks of the Andes Mountains at an elevation of 4200m, El Tatio is the third largest geyser field in the world. Its non-pristine condition makes it an ideal location to perform minutely invasive geophysical studies. The El Jefe Geyser was chosen for its easily accessible conduit and extremely periodic eruption cycle (~120s). During approximately 2 weeks of continuous recording, we recorded ~2500 nighttime eruptions which lack cultural noise from tourism. With ample data, we aim to study how the source varies spatially and temporally during each phase of the geyser's eruption cycle. We are developing a new back-projection processing technique to improve source imaging for diffuse signals. Our method was previously applied to the Sierra Negra Volcano system, which also exhibits repeating harmonic and diffuse seismic sources. We back-project correlated seismic signals from the receivers back to their sources, assuming linear source to receiver paths and a known velocity model (obtained from ambient noise tomography). We apply polarization filters to isolate individual and concurrent geyser energy associated with P and S phases. We generate 4D, time-lapsed images of the geyser source field that illustrate how the source distribution changes through the eruption cycle. We compare images for pre-eruption, co-eruption, post-eruption and quiescent periods. We use our images to assess eruption mechanics in the system (i.e. top-down vs. bottom-up) and

A Multiresolution Image Completion Algorithm for Compressing Digital Color Images

Directory of Open Access Journals (Sweden)

R. Gomathi

2014-01-01

Full Text Available This paper introduces a new framework for image coding that uses image inpainting method. In the proposed algorithm, the input image is subjected to image analysis to remove some of the portions purposefully. At the same time, edges are extracted from the input image and they are passed to the decoder in the compressed manner. The edges which are transmitted to decoder act as assistant information and they help inpainting process fill the missing regions at the decoder. Textural synthesis and a new shearlet inpainting scheme based on the theory of p-Laplacian operator are proposed for image restoration at the decoder. Shearlets have been mathematically proven to represent distributed discontinuities such as edges better than traditional wavelets and are a suitable tool for edge characterization. This novel shearlet p-Laplacian inpainting model can effectively reduce the staircase effect in Total Variation (TV inpainting model whereas it can still keep edges as well as TV model. In the proposed scheme, neural network is employed to enhance the value of compression ratio for image coding. Test results are compared with JPEG 2000 and H.264 Intracoding algorithms. The results show that the proposed algorithm works well.

Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

Directory of Open Access Journals (Sweden)

Duo Hao

2017-11-01

Full Text Available Cameras mounted on vehicles frequently suffer from image shake due to the vehicles’ motions. To remove jitter motions and preserve intentional motions, a hybrid digital image stabilization method is proposed that uses variational mode decomposition (VMD and relative entropy (RE. In this paper, the global motion vector (GMV is initially decomposed into several narrow-banded modes by VMD. REs, which exhibit the difference of probability distribution between two modes, are then calculated to identify the intentional and jitter motion modes. Finally, the summation of the jitter motion modes constitutes jitter motions, whereas the subtraction of the resulting sum from the GMV represents the intentional motions. The proposed stabilization method is compared with several known methods, namely, medium filter (MF, Kalman filter (KF, wavelet decomposition (MD method, empirical mode decomposition (EMD-based method, and enhanced EMD-based method, to evaluate stabilization performance. Experimental results show that the proposed method outperforms the other stabilization methods.

Thickness of patellofemoral articular cartilage as measured on MR imaging: sequence comparison of accuracy, reproducibility, and interobserver variation

Energy Technology Data Exchange (ETDEWEB)

Van Leersum, M.D. [Dept. of Radiology, Thomas Jefferson Univ. Hospital, Philadelphia, PA (United States); Schweitzer, M.E. [Dept. of Radiology, Thomas Jefferson Univ. Hospital, Philadelphia, PA (United States); Gannon, F. [Dept. of Pathology, Thomas Jefferson Univ. Hospital, Philadelphia, PA (United States); Vinitski, S. [Dept. of Radiology, Thomas Jefferson Univ. Hospital, Philadelphia, PA (United States); Finkel, G. [Dept. of Pathology, Thomas Jefferson Univ. Hospital, Philadelphia, PA (United States); Mitchell, D.G. [Dept. of Radiology, Thomas Jefferson Univ. Hospital, Philadelphia, PA (United States)

1995-08-01

This study was undertaken to assess the accuracy, precision, and reliability of magnetic resonance (MR) measurements of articular cartilage. Fifteen cadaveric patellas were imaged in the axial plane at 1.5 T. Gradient echo and fat-suppressed FSE, T2-weighted, proton density, and T1-weighted sequences were performed. We measured each 5-mm section separately at three standardized positions, giving a total of 900 measurements. These findings were correlated with independently performed measurements of the corresponding anatomic sections. A hundred random measurements were also evaluated for reproducibility and interobserver variation. Although all sequences were highly accurate, the T1-weighted images were the most accurate, with a mean difference of 0.25 mm and a correlation coefficient of 0.85. All sequences were also highly reproducible with little inter-observer variation. In an attempt to improve the accuracy of the MR measurements further, we retrospectively evaluated all measurements with discrepancies greater than 1 mm from the specimen. All these differences were attributable to focal defects causing exaggeration of the thickness on MR imaging. (orig.)

Thickness of patellofemoral articular cartilage as measured on MR imaging: sequence comparison of accuracy, reproducibility, and interobserver variation

International Nuclear Information System (INIS)

Van Leersum, M.D.; Schweitzer, M.E.; Gannon, F.; Vinitski, S.; Finkel, G.; Mitchell, D.G.

1995-01-01

This study was undertaken to assess the accuracy, precision, and reliability of magnetic resonance (MR) measurements of articular cartilage. Fifteen cadaveric patellas were imaged in the axial plane at 1.5 T. Gradient echo and fat-suppressed FSE, T2-weighted, proton density, and T1-weighted sequences were performed. We measured each 5-mm section separately at three standardized positions, giving a total of 900 measurements. These findings were correlated with independently performed measurements of the corresponding anatomic sections. A hundred random measurements were also evaluated for reproducibility and interobserver variation. Although all sequences were highly accurate, the T1-weighted images were the most accurate, with a mean difference of 0.25 mm and a correlation coefficient of 0.85. All sequences were also highly reproducible with little inter-observer variation. In an attempt to improve the accuracy of the MR measurements further, we retrospectively evaluated all measurements with discrepancies greater than 1 mm from the specimen. All these differences were attributable to focal defects causing exaggeration of the thickness on MR imaging. (orig.)

Modeling per capita state health expenditure variation: state-level characteristics matter.

Science.gov (United States)

Cuckler, Gigi; Sisko, Andrea

2013-01-01

In this paper, we describe the methods underlying the econometric model developed by the Office of the Actuary in the Centers for Medicare & Medicaid Services, to explain differences in per capita total personal health care spending by state, as described in Cuckler, et al. (2011). Additionally, we discuss many alternative model specifications to provide additional insights for valid interpretation of the model. We study per capita personal health care spending as measured by the State Health Expenditures, by State of Residence for 1991-2009, produced by the Centers for Medicare & Medicaid Services' Office of the Actuary. State-level demographic, health status, economic, and health economy characteristics were gathered from a variety of U.S. government sources, such as the Census Bureau, Bureau of Economic Analysis, the Centers for Disease Control, the American Hospital Association, and HealthLeaders-InterStudy. State-specific factors, such as income, health care capacity, and the share of elderly residents, are important factors in explaining the level of per capita personal health care spending variation among states over time. However, the slow-moving nature of health spending per capita and close relationships among state-level factors create inefficiencies in modeling this variation, likely resulting in incorrectly estimated standard errors. In addition, we find that both pooled and fixed effects models primarily capture cross-sectional variation rather than period-specific variation.

Modeling fish community dynamics in Florida Everglades: Role of temperature variation

Science.gov (United States)

Al-Rabai'ah, H. A.; Koh, H. L.; DeAngelis, Donald L.; Lee, Hooi-Ling

2002-01-01

Temperature variation is an important factor in Everglade wetlands ecology. A temperature fluctuation from 17°C to 32°C recorded in the Everglades may have significant impact on fish dynamics. The short life cycles of some of Everglade fishes has rendered this temperature variation to have even more impacts on the ecosystem. Fish population dynamic models, which do not explicitly consider seasonal oscillations in temperature, may fail to describe the details of such a population. Hence, a model for fish in freshwater marshes of the Florida Everglades that explicitly incorporates seasonal temperature variations is developed. The model's main objective is to assess the temporal pattern of fish population and densities through time subject to temperature variations. Fish population is divided into 2 functional groups (FGs) consisting of small fishes; each group is subdivided into 5-day age classes during their life cycles. Many governing sub-modules are set directly or indirectly to be temperature dependent. Growth, fecundity, prey availability, consumption rates and mortality are examples. Several mortality sub-modules are introduced in the model, of which starvation mortality is set to be proportional to the ratio of prey needed to prey available at that particular time step. As part of the calibration process, the model is run for 50 years to ensure that fish densities do not go to extinction, while the simulation period is about 8 years.

A chromaticity-brightness model for color images denoising in a Meyer’s “u + v” framework

KAUST Repository

Ferreira, Rita; Fonseca, Irene; Mascarenhas, M. Luí sa

2017-01-01

A variational model for imaging segmentation and denoising color images is proposed. The model combines Meyer’s “u+v” decomposition with a chromaticity-brightness framework and is expressed by a minimization of energy integral functionals depending on a small parameter ε>0. The asymptotic behavior as ε→0+ is characterized, and convergence of infima, almost minimizers, and energies are established. In particular, an integral representation of the lower semicontinuous envelope, with respect to the L1-norm, of functionals with linear growth and defined for maps taking values on a certain compact manifold is provided. This study escapes the realm of previous results since the underlying manifold has boundary, and the integrand and its recession function fail to satisfy hypotheses commonly assumed in the literature. The main tools are Γ-convergence and relaxation techniques.

A chromaticity-brightness model for color images denoising in a Meyer’s “u + v” framework

KAUST Repository

Ferreira, Rita

2017-09-11

A variational model for imaging segmentation and denoising color images is proposed. The model combines Meyer’s “u+v” decomposition with a chromaticity-brightness framework and is expressed by a minimization of energy integral functionals depending on a small parameter ε>0. The asymptotic behavior as ε→0+ is characterized, and convergence of infima, almost minimizers, and energies are established. In particular, an integral representation of the lower semicontinuous envelope, with respect to the L1-norm, of functionals with linear growth and defined for maps taking values on a certain compact manifold is provided. This study escapes the realm of previous results since the underlying manifold has boundary, and the integrand and its recession function fail to satisfy hypotheses commonly assumed in the literature. The main tools are Γ-convergence and relaxation techniques.

Target Selection Models with Preference Variation Between Offenders

NARCIS (Netherlands)

Townsley, Michael; Birks, Daniel; Ruiter, Stijn; Bernasco, Wim; White, Gentry

2016-01-01

Objectives: This study explores preference variation in location choice strategies of residential burglars. Applying a model of offender target selection that is grounded in assertions of the routine activity approach, rational choice perspective, crime pattern and social disorganization theories,

Micro-CT image reconstruction based on alternating direction augmented Lagrangian method and total variation.

Science.gov (United States)

Gopi, Varun P; Palanisamy, P; Wahid, Khan A; Babyn, Paul; Cooper, David

2013-01-01

Micro-computed tomography (micro-CT) plays an important role in pre-clinical imaging. The radiation from micro-CT can result in excess radiation exposure to the specimen under test, hence the reduction of radiation from micro-CT is essential. The proposed research focused on analyzing and testing an alternating direction augmented Lagrangian (ADAL) algorithm to recover images from random projections using total variation (TV) regularization. The use of TV regularization in compressed sensing problems makes the recovered image quality sharper by preserving the edges or boundaries more accurately. In this work TV regularization problem is addressed by ADAL which is a variant of the classic augmented Lagrangian method for structured optimization. The per-iteration computational complexity of the algorithm is two fast Fourier transforms, two matrix vector multiplications and a linear time shrinkage operation. Comparison of experimental results indicate that the proposed algorithm is stable, efficient and competitive with the existing algorithms for solving TV regularization problems. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

Science.gov (United States)

Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

Validation of Diagnostic Imaging Based on Repeat Examinations. An Image Interpretation Model

International Nuclear Information System (INIS)

Isberg, B.; Jorulf, H.; Thorstensen, Oe.

2004-01-01

Purpose: To develop an interpretation model, based on repeatedly acquired images, aimed at improving assessments of technical efficacy and diagnostic accuracy in the detection of small lesions. Material and Methods: A theoretical model is proposed. The studied population consists of subjects that develop focal lesions which increase in size in organs of interest during the study period. The imaging modality produces images that can be re-interpreted with high precision, e.g. conventional radiography, computed tomography, and magnetic resonance imaging. At least four repeat examinations are carried out. Results: The interpretation is performed in four or five steps: 1. Independent readers interpret the examinations chronologically without access to previous or subsequent films. 2. Lesions found on images at the last examination are included in the analysis, with interpretation in consensus. 3. By concurrent back-reading in consensus, the lesions are identified on previous images until they are so small that even in retrospect they are undetectable. The earliest examination at which included lesions appear is recorded, and the lesions are verified by their growth (imaging reference standard). Lesion size and other characteristics may be recorded. 4. Records made at step 1 are corrected to those of steps 2 and 3. False positives are recorded. 5. (Optional) Lesion type is confirmed by another diagnostic test. Conclusion: Applied on subjects with progressive disease, the proposed image interpretation model may improve assessments of technical efficacy and diagnostic accuracy in the detection of small focal lesions. The model may provide an accurate imaging reference standard as well as repeated detection rates and false-positive rates for tested imaging modalities. However, potential review bias necessitates a strict protocol

3D Facial Landmarking under Expression, Pose, and Occlusion Variations

NARCIS (Netherlands)

H. Dibeklioğ lu; A.A. Salah (Albert Ali); L. Akarun

2008-01-01

htmlabstractAutomatic localization of 3D facial features is important for face recognition, tracking, modeling and expression analysis. Methods developed for 2D images were shown to have problems working across databases acquired with different illumination conditions. Expression variations, pose

Potential of electrical resistivity tomography and muon density imaging to study spatio-temporal variations in the sub-surface

Science.gov (United States)

Lesparre, Nolwenn; Cabrera, Justo; Courbet, Christelle

2015-04-01

We explore the capacity of electrical resistivity tomography and muon density imaging to detect spatio-temporal variations of the medium surrounding a regional fault crossing the underground platform of Tournemire (Aveyron, France). The studied Cernon fault is sub-vertical and intersects perpendicularly the tunnel of Tournemire and extends to surface. The fault separates clay and limestones layers of the Dogger from limestones layers of the Lias. The Cernon fault presents a thickness of a ten of meters and drives water from an aquifer circulating at the top of the Dogger clay layer to the tunnel. An experiment combining electrical resistivity imaging and muon density imaging was setup taking advantage of the tunnel presence. A specific array of electrodes were set up, adapted for the characterization of the fault. Electrodes were placed along the tunnel as well as at the surface above the tunnel on both sides of the fault in order to acquire data in transmission across the massif to better cover the sounded medium. Electrical resistivity is particularly sensitive to water presence in the medium and thus carry information on the main water flow paths and on the pore space saturation. At the same time a muon sensor was placed in the tunnel under the fault region to detect muons coming from the sky after their crossing of the rock medium. Since the muon flux is attenuated as function of the quantity of matter crossed, muons flux measurements supply information on the medium average density along muons paths. The sensor presents 961 angles of view so measurements performed from one station allows a comparison of the muon flux temporal variations along the fault as well as in the medium surrounding the fault. As the water saturation of the porous medium fluctuates through time the medium density might indeed present sensible variations as shown by gravimetric studies. During the experiment important rainfalls occurred leading variations of the medium properties

A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction

Science.gov (United States)

Huang, Xiaokun; Zhang, You; Wang, Jing

2018-02-01

Reconstructing four-dimensional cone-beam computed tomography (4D-CBCT) images directly from respiratory phase-sorted traditional 3D-CBCT projections can capture target motion trajectory, reduce motion artifacts, and reduce imaging dose and time. However, the limited numbers of projections in each phase after phase-sorting decreases CBCT image quality under traditional reconstruction techniques. To address this problem, we developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, an iterative method that can reconstruct higher quality 4D-CBCT images from limited projections using an inter-phase intensity-driven motion model. However, the accuracy of the intensity-driven motion model is limited in regions with fine details whose quality is degraded due to insufficient projection number, which consequently degrades the reconstructed image quality in corresponding regions. In this study, we developed a new 4D-CBCT reconstruction algorithm by introducing biomechanical modeling into SMEIR (SMEIR-Bio) to boost the accuracy of the motion model in regions with small fine structures. The biomechanical modeling uses tetrahedral meshes to model organs of interest and solves internal organ motion using tissue elasticity parameters and mesh boundary conditions. This physics-driven approach enhances the accuracy of solved motion in the organ’s fine structures regions. This study used 11 lung patient cases to evaluate the performance of SMEIR-Bio, making both qualitative and quantitative comparisons between SMEIR-Bio, SMEIR, and the algebraic reconstruction technique with total variation regularization (ART-TV). The reconstruction results suggest that SMEIR-Bio improves the motion model’s accuracy in regions containing small fine details, which consequently enhances the accuracy and quality of the reconstructed 4D-CBCT images.

A variational ensemble scheme for noisy image data assimilation

Science.gov (United States)

Yang, Yin; Robinson, Cordelia; Heitz, Dominique; Mémin, Etienne

2014-05-01

Data assimilation techniques aim at recovering a system state variables trajectory denoted as X, along time from partially observed noisy measurements of the system denoted as Y. These procedures, which couple dynamics and noisy measurements of the system, fulfill indeed a twofold objective. On one hand, they provide a denoising - or reconstruction - procedure of the data through a given model framework and on the other hand, they provide estimation procedures for unknown parameters of the dynamics. A standard variational data assimilation problem can be formulated as the minimization of the following objective function with respect to the initial discrepancy, η, from the background initial guess: δ« J(η(x)) = 1∥Xb (x) - X (t ,x)∥2 + 1 tf∥H(X (t,x ))- Y (t,x)∥2dt. 2 0 0 B 2 t0 R (1) where the observation operator H links the state variable and the measurements. The cost function can be interpreted as the log likelihood function associated to the a posteriori distribution of the state given the past history of measurements and the background. In this work, we aim at studying ensemble based optimal control strategies for data assimilation. Such formulation nicely combines the ingredients of ensemble Kalman filters and variational data assimilation (4DVar). It is also formulated as the minimization of the objective function (1), but similarly to ensemble filter, it introduces in its objective function an empirical ensemble-based background-error covariance defined as: B ≡ )(Xb - )T>. (2) Thus, it works in an off-line smoothing mode rather than on the fly like sequential filters. Such resulting ensemble variational data assimilation technique corresponds to a relatively new family of methods [1,2,3]. It presents two main advantages: first, it does not require anymore to construct the adjoint of the dynamics tangent linear operator, which is a considerable advantage with respect to the method's implementation, and second, it enables the handling of a flow

Validity of covariance models for the analysis of geographical variation

DEFF Research Database (Denmark)

Guillot, Gilles; Schilling, Rene L.; Porcu, Emilio

2014-01-01

1. Due to the availability of large molecular data-sets, covariance models are increasingly used to describe the structure of genetic variation as an alternative to more heavily parametrised biological models. 2. We focus here on a class of parametric covariance models that received sustained att...

Reduce in Variation and Improve Efficiency of Target Volume Delineation by a Computer-Assisted System Using a Deformable Image Registration Approach

International Nuclear Information System (INIS)

Chao, K.S. Clifford; Bhide, Shreerang FRCR; Chen, Hansen; Asper, Joshua PAC; Bush, Steven; Franklin, Gregg; Kavadi, Vivek; Liengswangwong, Vichaivood; Gordon, William; Raben, Adam; Strasser, Jon; Koprowski, Christopher; Frank, Steven; Chronowski, Gregory; Ahamad, Anesa; Malyapa, Robert; Zhang Lifei; Dong Lei

2007-01-01

Purpose: To determine whether a computer-assisted target volume delineation (CAT) system using a deformable image registration approach can reduce the variation of target delineation among physicians with different head and neck (HN) IMRT experiences and reduce the time spent on the contouring process. Materials and Methods: We developed a deformable image registration method for mapping contours from a template case to a patient case with a similar tumor manifestation but different body configuration. Eight radiation oncologists with varying levels of clinical experience in HN IMRT performed target delineation on two HN cases, one with base-of-tongue (BOT) cancer and another with nasopharyngeal cancer (NPC), by first contouring from scratch and then by modifying the contours deformed by the CAT system. The gross target volumes were provided. Regions of interest for comparison included the clinical target volumes (CTVs) and normal organs. The volumetric and geometric variation of these regions of interest and the time spent on contouring were analyzed. Results: We found that the variation in delineating CTVs from scratch among the physicians was significant, and that using the CAT system reduced volumetric variation and improved geometric consistency in both BOT and NPC cases. The average timesaving when using the CAT system was 26% to 29% for more experienced physicians and 38% to 47% for the less experienced ones. Conclusions: A computer-assisted target volume delineation approach, using a deformable image-registration method with template contours, was able to reduce the variation among physicians with different experiences in HN IMRT while saving contouring time

Variational Boussinesq model for strongly nonlinear dispersive waves

NARCIS (Netherlands)

Lawrence, C.; Adytia, D.; van Groesen, E.

2018-01-01

For wave tank, coastal and oceanic applications, a fully nonlinear Variational Boussinesq model with optimized dispersion is derived and a simple Finite Element implementation is described. Improving a previous weakly nonlinear version, high waves over flat and varying bottom are shown to be

Parametric uncertainty in optical image modeling

Science.gov (United States)

Potzick, James; Marx, Egon; Davidson, Mark

2006-10-01

Optical photomask feature metrology and wafer exposure process simulation both rely on optical image modeling for accurate results. While it is fair to question the accuracies of the available models, model results also depend on several input parameters describing the object and imaging system. Errors in these parameter values can lead to significant errors in the modeled image. These parameters include wavelength, illumination and objective NA's, magnification, focus, etc. for the optical system, and topography, complex index of refraction n and k, etc. for the object. In this paper each input parameter is varied over a range about its nominal value and the corresponding images simulated. Second order parameter interactions are not explored. Using the scenario of the optical measurement of photomask features, these parametric sensitivities are quantified by calculating the apparent change of the measured linewidth for a small change in the relevant parameter. Then, using reasonable values for the estimated uncertainties of these parameters, the parametric linewidth uncertainties can be calculated and combined to give a lower limit to the linewidth measurement uncertainty for those parameter uncertainties.

A model reduction approach for the variational estimation of vascular compliance by solving an inverse fluid–structure interaction problem

International Nuclear Information System (INIS)

Bertagna, Luca; Veneziani, Alessandro

2014-01-01

Scientific computing has progressively become an important tool for research in cardiovascular diseases. The role of quantitative analyses based on numerical simulations has moved from ‘proofs of concept’ to patient-specific investigations, thanks to a strong integration between imaging and computational tools. However, beyond individual geometries, numerical models require the knowledge of parameters that are barely retrieved from measurements, especially in vivo. For this reason, recently cardiovascular mathematics considered data assimilation procedures for extracting the knowledge of patient-specific parameters from measures and images. In this paper, we consider specifically the quantification of vascular compliance, i.e. the parameter quantifying the tendency of arterial walls to deform under blood stress. Following up a previous paper, where a variational data assimilation procedure was proposed, based on solving an inverse fluid–structure interaction problem, here we consider model reduction techniques based on a proper orthogonal decomposition approach to accomplish the solution of the inverse problem in a computationally efficient way. (paper)

Diffusion tensor imaging of the human calf: Variation of inter- and intramuscle-specific diffusion parameters.

Science.gov (United States)

Schlaffke, Lara; Rehmann, Robert; Froeling, Martijn; Kley, Rudolf; Tegenthoff, Martin; Vorgerd, Matthias; Schmidt-Wilcke, Tobias

2017-10-01

To investigate to what extent inter- and intramuscular variations of diffusion parameters of human calf muscles can be explained by age, gender, muscle location, and body mass index (BMI) in a specific age group (20-35 years). Whole calf muscles of 18 healthy volunteers were evaluated. Magnetic resonance imaging (MRI) was performed using a 3T scanner and a 16-channel Torso XL coil. Diffusion-weighted images were acquired to perform fiber tractography and diffusion tensor imaging (DTI) analysis for each muscle of both legs. Fiber tractography was used to separate seven lower leg muscles. Associations between DTI parameters and confounds were evaluated. All muscles were additionally separated in seven identical segments along the z-axis to evaluate intramuscular differences in diffusion parameters. Fractional anisotropy (FA) and mean diffusivity (MD) were obtained for each muscle with low standard deviations (SDs) (SD FA : 0.01-0.02; SD MD : 0.07-0.14(10 -3 )). We found significant differences in FA values of the tibialis anterior muscle (AT) and extensor digitorum longus (EDL) muscles between men and women for whole muscle FA (two-sample t-tests; AT: P = 0.0014; EDL: P = 0.0004). We showed significant intramuscular differences in diffusion parameters between adjacent segments in most calf muscles (P < 0.001). Whereas muscle insertions showed higher (SD 0.03-0.06) than muscle bellies (SD 0.01-0.03), no relationships between FA or MD with age or BMI were found. Inter- and intramuscular variations in diffusion parameters of the calf were shown, which are not related to age or BMI in this age group. Differences between muscle belly and insertion should be considered when interpreting datasets not including whole muscles. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1137-1148. © 2017 International Society for Magnetic Resonance in Medicine.

Non-rigid image registration using bone growth model

DEFF Research Database (Denmark)

Bro-Nielsen, Morten; Gramkow, Claus; Kreiborg, Sven

1997-01-01

Non-rigid registration has traditionally used physical models like elasticity and fluids. These models are very seldom valid models of the difference between the registered images. This paper presents a non-rigid registration algorithm, which uses a model of bone growth as a model of the change...... between time sequence images of the human mandible. By being able to register the images, this paper at the same time contributes to the validation of the growth model, which is based on the currently available medical theories and knowledge...

Model-Based Reconstructive Elasticity Imaging Using Ultrasound

Directory of Open Access Journals (Sweden)

Salavat R. Aglyamov

2007-01-01

Full Text Available Elasticity imaging is a reconstructive imaging technique where tissue motion in response to mechanical excitation is measured using modern imaging systems, and the estimated displacements are then used to reconstruct the spatial distribution of Young's modulus. Here we present an ultrasound elasticity imaging method that utilizes the model-based technique for Young's modulus reconstruction. Based on the geometry of the imaged object, only one axial component of the strain tensor is used. The numerical implementation of the method is highly efficient because the reconstruction is based on an analytic solution of the forward elastic problem. The model-based approach is illustrated using two potential clinical applications: differentiation of liver hemangioma and staging of deep venous thrombosis. Overall, these studies demonstrate that model-based reconstructive elasticity imaging can be used in applications where the geometry of the object and the surrounding tissue is somewhat known and certain assumptions about the pathology can be made.

Achilles tendons from decorin- and biglycan-null mouse models have inferior mechanical and structural properties predicted by an image-based empirical damage model.

Science.gov (United States)

Gordon, J A; Freedman, B R; Zuskov, A; Iozzo, R V; Birk, D E; Soslowsky, L J

2015-07-16

Achilles tendons are a common source of pain and injury, and their pathology may originate from aberrant structure function relationships. Small leucine rich proteoglycans (SLRPs) influence mechanical and structural properties in a tendon-specific manner. However, their roles in the Achilles tendon have not been defined. The objective of this study was to evaluate the mechanical and structural differences observed in mouse Achilles tendons lacking class I SLRPs; either decorin or biglycan. In addition, empirical modeling techniques based on mechanical and image-based measures were employed. Achilles tendons from decorin-null (Dcn(-/-)) and biglycan-null (Bgn(-/-)) C57BL/6 female mice (N=102) were used. Each tendon underwent a dynamic mechanical testing protocol including simultaneous polarized light image capture to evaluate both structural and mechanical properties of each Achilles tendon. An empirical damage model was adapted for application to genetic variation and for use with image based structural properties to predict tendon dynamic mechanical properties. We found that Achilles tendons lacking decorin and biglycan had inferior mechanical and structural properties that were age dependent; and that simple empirical models, based on previously described damage models, were predictive of Achilles tendon dynamic modulus in both decorin- and biglycan-null mice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Generative Interpretation of Medical Images

DEFF Research Database (Denmark)

Stegmann, Mikkel Bille

2004-01-01

This thesis describes, proposes and evaluates methods for automated analysis and quantification of medical images. A common theme is the usage of generative methods, which draw inference from unknown images by synthesising new images having shape, pose and appearance similar to the analysed images......, handling of non-Gaussian variation by means of cluster analysis, correction of respiratory noise in cardiac MRI, and the extensions to multi-slice two-dimensional time-series and bi-temporal three-dimensional models. The medical applications include automated estimation of: left ventricular ejection...

Biosphere modelling for a HLW repository - scenario and parameter variations

International Nuclear Information System (INIS)

Grogan, H.

1985-03-01

In Switzerland high-level radioactive wastes have been considered for disposal in deep-lying crystalline formations. The individual doses to man resulting from radionuclides entering the biosphere via groundwater transport are calculated. The main recipient area modelled, which constitutes the base case, is a broad gravel terrace sited along the south bank of the river Rhine. An alternative recipient region, a small valley with a well, is also modelled. A number of parameter variations are performed in order to ascertain their impact on the doses. Finally two scenario changes are modelled somewhat simplistically, these consider different prevailing climates, namely tundra and a warmer climate than present. In the base case negligibly low doses to man in the long term, resulting from the existence of a HLW repository have been calculated. Cs-135 results in the largest dose (8.4E-7 mrem/y at 6.1E+6 y) while Np-237 gives the largest dose from the actinides (3.6E-8 mrem/y). The response of the model to parameter variations cannot be easily predicted due to non-linear coupling of many of the parameters. However, the calculated doses were negligibly low in all cases as were those resulting from the two scenario variations. (author)

Ultrasonic-assisted manufacturing processes: Variational model and numerical simulations

KAUST Repository

Siddiq, Amir; El Sayed, Tamer

2012-01-01

We present a computational study of ultrasonic assisted manufacturing processes including sheet metal forming, upsetting, and wire drawing. A fully variational porous plasticity model is modified to include ultrasonic softening effects

Semi-Automatic Classification Of Histopathological Images: Dealing With Inter-Slide Variations

Directory of Open Access Journals (Sweden)

Michael Gadermayr

2016-06-01

In case of 50 available labelled sample patches of a certain whole slide image, the overall classification rate increased from 92 % to 98 % through including the interactive labelling step. Even with only 20 labelled patches, accuracy already increased to 97 %. Without a pre-trained model, if training is performed on target domain data only, 88 % (20 labelled samples and 95 % (50 labelled samples accuracy, respectively, were obtained. If enough target domain data was available (about 20 images, the amount of source domain data was of minor relevance. The difference in outcome between a source domain training data set containing 100 patches from one whole slide image and a set containing 700 patches from seven images was lower than 1 %. Contrarily, without target domain data, the difference in accuracy was 10 % (82 % compared to 92 % between these two settings. Execution runtime between two interaction steps is significantly below one second (0.23 s, which is an important usability criterion. It proved to be beneficial to select specific target domain data in an active learning sense based on the currently available trained model. While experimental evaluation provided strong empirical evidence for increased classification performance with the proposed method, the additional manual effort can be kept at a low level. The labelling of e.g. 20 images per slide is surely less time consuming than the validation of a complete whole slide image processed with a fully automatic, but less reliable, segmentation approach. Finally, it should be highlighted that the proposed interaction protocol could easily be adapted to other histopathological classification or segmentation tasks, also for implementation in a clinical system.  

Constraining spatial variations of the fine-structure constant in symmetron models

Directory of Open Access Journals (Sweden)

A.M.M. Pinho

2017-06-01

Full Text Available We introduce a methodology to test models with spatial variations of the fine-structure constant α, based on the calculation of the angular power spectrum of these measurements. This methodology enables comparisons of observations and theoretical models through their predictions on the statistics of the α variation. Here we apply it to the case of symmetron models. We find no indications of deviations from the standard behavior, with current data providing an upper limit to the strength of the symmetron coupling to gravity (log⁡β2<−0.9 when this is the only free parameter, and not able to constrain the model when also the symmetry breaking scale factor aSSB is free to vary.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

Directory of Open Access Journals (Sweden)

Hongyang Lu

2016-01-01

Full Text Available Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV approach and adaptive dictionary learning (DL. In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

Image Size Variation Influence on Corrupted and Non-viewable BMP Image

Science.gov (United States)

Azmi, Tengku Norsuhaila T.; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Hamid, Isredza Rahmi A.; Chai Wen, Chuah

2017-08-01

Image is one of the evidence component seek in digital forensics. Joint Photographic Experts Group (JPEG) format is most popular used in the Internet because JPEG files are very lossy and easy to compress that can speed up Internet transmitting processes. However, corrupted JPEG images are hard to recover due to the complexities of determining corruption point. Nowadays Bitmap (BMP) images are preferred in image processing compared to another formats because BMP image contain all the image information in a simple format. Therefore, in order to investigate the corruption point in JPEG, the file is required to be converted into BMP format. Nevertheless, there are many things that can influence the corrupting of BMP image such as the changes of image size that make the file non-viewable. In this paper, the experiment indicates that the size of BMP file influences the changes in the image itself through three conditions, deleting, replacing and insertion. From the experiment, we learnt by correcting the file size, it can able to produce a viewable file though partially. Then, it can be investigated further to identify the corruption point.

A variational image-based approach to the correction of susceptibility artifacts in the alignment of diffusion weighted and structural MRI.

Science.gov (United States)

Tao, Ran; Fletcher, P Thomas; Gerber, Samuel; Whitaker, Ross T

2009-01-01

This paper presents a method for correcting the geometric and greyscale distortions in diffusion-weighted MRI that result from inhomogeneities in the static magnetic field. These inhomogeneities may due to imperfections in the magnet or to spatial variations in the magnetic susceptibility of the object being imaged--so called susceptibility artifacts. Echo-planar imaging (EPI), used in virtually all diffusion weighted acquisition protocols, assumes a homogeneous static field, which generally does not hold for head MRI. The resulting distortions are significant, sometimes more than ten millimeters. These artifacts impede accurate alignment of diffusion images with structural MRI, and are generally considered an obstacle to the joint analysis of connectivity and structure in head MRI. In principle, susceptibility artifacts can be corrected by acquiring (and applying) a field map. However, as shown in the literature and demonstrated in this paper, field map corrections of susceptibility artifacts are not entirely accurate and reliable, and thus field maps do not produce reliable alignment of EPIs with corresponding structural images. This paper presents a new, image-based method for correcting susceptibility artifacts. The method relies on a variational formulation of the match between an EPI baseline image and a corresponding T2-weighted structural image but also specifically accounts for the physics of susceptibility artifacts. We derive a set of partial differential equations associated with the optimization, describe the numerical methods for solving these equations, and present results that demonstrate the effectiveness of the proposed method compared with field-map correction.

EVALUATION OF RATIONAL FUNCTION MODEL FOR GEOMETRIC MODELING OF CHANG'E-1 CCD IMAGES

Directory of Open Access Journals (Sweden)

Y. Liu

2012-08-01

Full Text Available Rational Function Model (RFM is a generic geometric model that has been widely used in geometric processing of high-resolution earth-observation satellite images, due to its generality and excellent capability of fitting complex rigorous sensor models. In this paper, the feasibility and precision of RFM for geometric modeling of China's Chang'E-1 (CE-1 lunar orbiter images is presented. The RFM parameters of forward-, nadir- and backward-looking CE-1 images are generated though least squares solution using virtual control points derived from the rigorous sensor model. The precision of the RFM is evaluated by comparing with the rigorous sensor model in both image space and object space. Experimental results using nine images from three orbits show that RFM can precisely fit the rigorous sensor model of CE-1 CCD images with a RMS residual error of 1/100 pixel level in image space and less than 5 meters in object space. This indicates that it is feasible to use RFM to describe the imaging geometry of CE-1 CCD images and spacecraft position and orientation. RFM will enable planetary data centers to have an option to supply RFM parameters of orbital images while keeping the original orbit trajectory data confidential.

Preclinical Magnetic Resonance Fingerprinting (MRF) at 7 T: Effective Quantitative Imaging for Rodent Disease Models

Science.gov (United States)

Gao, Ying; Chen, Yong; Ma, Dan; Jiang, Yun; Herrmann, Kelsey A.; Vincent, Jason A.; Dell, Katherine M.; Drumm, Mitchell L.; Brady-Kalnay, Susann M.; Griswold, Mark A.; Flask, Chris A.; Lu, Lan

2015-01-01

High field, preclinical magnetic resonance imaging (MRI) scanners are now commonly used to quantitatively assess disease status and efficacy of novel therapies in a wide variety of rodent models. Unfortunately, conventional MRI methods are highly susceptible to respiratory and cardiac motion artifacts resulting in potentially inaccurate and misleading data. We have developed an initial preclinical, 7.0 T MRI implementation of the highly novel Magnetic Resonance Fingerprinting (MRF) methodology that has been previously described for clinical imaging applications. The MRF technology combines a priori variation in the MRI acquisition parameters with dictionary-based matching of acquired signal evolution profiles to simultaneously generate quantitative maps of T1 and T2 relaxation times and proton density. This preclinical MRF acquisition was constructed from a Fast Imaging with Steady-state Free Precession (FISP) MRI pulse sequence to acquire 600 MRF images with both evolving T1 and T2 weighting in approximately 30 minutes. This initial high field preclinical MRF investigation demonstrated reproducible and differentiated estimates of in vitro phantoms with different relaxation times. In vivo preclinical MRF results in mouse kidneys and brain tumor models demonstrated an inherent resistance to respiratory motion artifacts as well as sensitivity to known pathology. These results suggest that MRF methodology may offer the opportunity for quantification of numerous MRI parameters for a wide variety of preclinical imaging applications. PMID:25639694

Testing the Processing Hypothesis of word order variation using a probabilistic language model

NARCIS (Netherlands)

Bloem, J.

2016-01-01

This work investigates the application of a measure of surprisal to modeling a grammatical variation phenomenon between near-synonymous constructions. We investigate a particular variation phenomenon, word order variation in Dutch two-verb clusters, where it has been established that word order

Empirical model of subdaily variations in the Earth rotation from GPS and its stability

Science.gov (United States)

Panafidina, N.; Kurdubov, S.; Rothacher, M.

2012-12-01

The model recommended by the IERS for these variations at diurnal and semidiurnal periods has been computed from an ocean tide model and comprises 71 terms in polar motion and Universal Time. In the present study we compute an empirical model of variations in the Earth rotation on tidal frequencies from homogeneously re-processed GPS-observations over 1994-2007 available as free daily normal equations. We discuss the reliability of the obtained amplitudes of the ERP variations and compare results from GPS and VLBI data to identify technique-specific problems and instabilities of the empirical tidal models.

On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

Science.gov (United States)

Abdelsamea, Mohammed M.; Gnecco, Giorgio; Gaber, Mohamed Medhat; Elyan, Eyad

2015-01-01

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses. PMID:25960736

A variational formulation for linear models in coupled dynamic thermoelasticity

International Nuclear Information System (INIS)

Feijoo, R.A.; Moura, C.A. de.

1981-07-01

A variational formulation for linear models in coupled dynamic thermoelasticity which quite naturally motivates the design of a numerical scheme for the problem, is studied. When linked to regularization or penalization techniques, this algorithm may be applied to more general models, namely, the ones that consider non-linear constraints associated to variational inequalities. The basic postulates of Mechanics and Thermodynamics as well as some well-known mathematical techniques are described. A thorough description of the algorithm implementation with the finite-element method is also provided. Proofs for existence and uniqueness of solutions and for convergence of the approximations are presented, and some numerical results are exhibited. (Author) [pt

Imaging regional variation of cellular proliferation in gliomas using 3'-deoxy-3'-[18F]fluorothymidine positron-emission tomography: an image-guided biopsy study

International Nuclear Information System (INIS)

Price, S.J.; Fryer, T.D.; Cleij, M.C.; Dean, A.F.; Joseph, J.; Salvador, R.; Wang, D.D.; Hutchinson, P.J.; Clark, J.C.; Burnet, N.G.; Pickard, J.D.; Aigbirhio, F.I.

2009-01-01

Aim: To compare regional variations in uptake of 3'-deoxy-3'- [ 18 F]-fluorothymidine (FLT) images using positron-emission tomography (PET) with measures of cellular proliferation from biopsy specimens obtained by image-guided brain biopsies. Materials and methods: Fourteen patients with a supratentorial glioma that required an image-guided brain biopsy were imaged preoperatively with dynamic PET after the administration of FLT. Maps of FLT irreversible uptake rate (K i ) and standardized uptake value (SUV) were calculated. These maps were co-registered to a gadolinium-enhanced T1-weighted spoiled gradient echo (SPGR) sequence that was used for biopsy guidance, and the mean and maximum K i and SUV determined for each biopsy site. These values were correlated with the MIB-1 labelling index (a tissue marker of proliferation) from these biopsy sites. Results: A total of 57 biopsy sites were studied. Although all measures correlated with MIB-1 labelling index, K i max provided the best correlation (Pearson coefficient, r = 0.68; p i mean (±SD) was significantly higher than in normal tissue (3.3 ± 1.7 x 10 -3 ml plasma /min/ml tissue versus 1.2 ± 0.7 x 10 -3 ml plasma /min/ml tissue ; p = 0.001). High-grade gliomas showed heterogeneous uptake with a mean K i of 7.7 ± 4 x 10 -3 ml plasma /min/ml tissue . A threshold K i mean of 1.8 x 10 -3 differentiates between normal tissue and tumour (sensitivity 84%, specificity 88%); however, the latter threshold underestimated the extent of tumour in half the cases. SUV closely agreed with K i measurements. Conclusion: FLT PET is a useful marker of cellular proliferation that correlates with regional variation in cellular proliferation; however, it is unable to identify the margin of gliomas

Seasonal variation in thoracic vessel calcifications: Evidence from a chest computed tomography study

Energy Technology Data Exchange (ETDEWEB)

Vehmas, Tapio; Leino-Arjas, Paeivi (Health and Work Ability, Finnish Inst. of Occupational Health, Helsinki (Finland)), e-mail: tapio.vehmas@ttl.fi; Hiltunen, Asta (Dept. of Radiology, Central Hospital of Laensi-Pohja, Kemi (Finland))

2010-01-15

Background: Cardiovascular disease incidence and mortality exhibit a winter peak and a summer trough, a fact that could have radiological manifestations. Purpose: To identify possible seasonal trends in the occurrence of thoracic vessel calcifications. Material and Methods: 505 male construction workers (aged 39-80 years) were each imaged once with chest spiral computed tomography (CT) during a 2-year period. Based on visual assessment of calcified plaques (0=no, 1=slight, 2=moderate, 3=extensive calcification), sum scores of atherosclerosis in coronary arteries, in the thoracic aorta, in the pre-cervical artery bases, and overall were constructed. The scores were regressed on the annual rank number of the CT day. Results: By using the cubic regression model, seasonal variation in calcified plaques in coronary arteries (P=0.003), in pre-cervical artery origins (P=0.015), and in the overall sum score (P=0.004) was observed. The peak occurred in January-February and the nadir in August. Depending on the model, about 2-3% of the variation in atherosclerotic calcifications could be explained by the season of imaging. Conclusion: The observed seasonal trend in calcifications parallels with mortality reports. Seasonal variations should be considered in atherosclerosis treatment studies. Confirmatory studies using modern imaging technology are needed in different countries and geographical locations, preferably with repeat imaging of the same individuals

Sensitivity of euphotic zone properties to CDOM variations in marine ecosystem models

OpenAIRE

Urtizberea, Agurtzane; Dupont, Nicolas; Rosland, Rune; Aksnes, Dag L.

2013-01-01

In marine ecosystem models, the underwater light intensity is commonly characterized by the shading of phytoplankton in addition to a background light attenuation coefficient. Colour dissolved organic matter (CDOM) is an important component of the background light attenuation, and we investigate how variation in CDOM attenuation affects euphotic zone properties in a general marine ecosystem model. Our results suggest that euphotic zone properties are highly sensitive to CDOM variations occurr...

A Proposed Conceptual Model to Measure Unwarranted Practice Variation

National Research Council Canada - National Science Library

Barr, Andrew M

2007-01-01

.... Employing a unit of analysis of the U.S. Army healthcare system and utilizing research by Wennberg and the Institute of Medicine, a model describing healthcare quality in terms of unwarranted practice variation and healthcare outcomes...

SU-F-J-178: A Computer Simulation Model Observer for Task-Based Image Quality Assessment in Radiation Therapy

International Nuclear Information System (INIS)

Dolly, S; Mutic, S; Anastasio, M; Li, H; Yu, L

2016-01-01

Purpose: Traditionally, image quality in radiation therapy is assessed subjectively or by utilizing physically-based metrics. Some model observers exist for task-based medical image quality assessment, but almost exclusively for diagnostic imaging tasks. As opposed to disease diagnosis, the task for image observers in radiation therapy is to utilize the available images to design and deliver a radiation dose which maximizes patient disease control while minimizing normal tissue damage. The purpose of this study was to design and implement a new computer simulation model observer to enable task-based image quality assessment in radiation therapy. Methods: A modular computer simulation framework was developed to resemble the radiotherapy observer by simulating an end-to-end radiation therapy treatment. Given images and the ground-truth organ boundaries from a numerical phantom as inputs, the framework simulates an external beam radiation therapy treatment and quantifies patient treatment outcomes using the previously defined therapeutic operating characteristic (TOC) curve. As a preliminary demonstration, TOC curves were calculated for various CT acquisition and reconstruction parameters, with the goal of assessing and optimizing simulation CT image quality for radiation therapy. Sources of randomness and bias within the system were analyzed. Results: The relationship between CT imaging dose and patient treatment outcome was objectively quantified in terms of a singular value, the area under the TOC (AUTOC) curve. The AUTOC decreases more rapidly for low-dose imaging protocols. AUTOC variation introduced by the dose optimization algorithm was approximately 0.02%, at the 95% confidence interval. Conclusion: A model observer has been developed and implemented to assess image quality based on radiation therapy treatment efficacy. It enables objective determination of appropriate imaging parameter values (e.g. imaging dose). Framework flexibility allows for incorporation

RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING

Directory of Open Access Journals (Sweden)

M.R. Renu

2013-02-01

Full Text Available High dynamic range imaging aims at creating an image with a range of intensity variations larger than the range supported by a camera sensor. Most commonly used methods combine multiple exposure low dynamic range (LDR images, to obtain the high dynamic range (HDR image. Available methods typically neglect the noise term while finding appropriate weighting functions to estimate the camera response function as well as the radiance map. We look at the HDR imaging problem in a denoising frame work and aim at reconstructing a low noise radiance map from noisy low dynamic range images, which is tone mapped to get the LDR equivalent of the HDR image. We propose a maximum aposteriori probability (MAP based reconstruction of the HDR image using Gibb’s prior to model the radiance map, with total variation (TV as the prior to avoid unnecessary smoothing of the radiance field. To make the computation with TV prior efficient, we extend the majorize-minimize method of upper bounding the total variation by a quadratic function to our case which has a nonlinear term arising from the camera response function. A theoretical justification for doing radiance domain denoising as opposed to image domain denoising is also provided.

Development and validation of a combined phased acoustical radiosity and image source model for predicting sound fields in rooms.

Science.gov (United States)

Marbjerg, Gerd; Brunskog, Jonas; Jeong, Cheol-Ho; Nilsson, Erling

2015-09-01

A model, combining acoustical radiosity and the image source method, including phase shifts on reflection, has been developed. The model is denoted Phased Acoustical Radiosity and Image Source Method (PARISM), and it has been developed in order to be able to model both specular and diffuse reflections with complex-valued and angle-dependent boundary conditions. This paper mainly describes the combination of the two models and the implementation of the angle-dependent boundary conditions. It furthermore describes how a pressure impulse response is obtained from the energy-based acoustical radiosity by regarding the model as being stochastic. Three methods of implementation are proposed and investigated, and finally, recommendations are made for their use. Validation of the image source method is done by comparison with finite element simulations of a rectangular room with a porous absorber ceiling. Results from the full model are compared with results from other simulation tools and with measurements. The comparisons of the full model are done for real-valued and angle-independent surface properties. The proposed model agrees well with both the measured results and the alternative theories, and furthermore shows a more realistic spatial variation than energy-based methods due to the fact that interference is considered.

Detection and Measurement of the Intracellular Calcium Variation in Follicular Cells

Directory of Open Access Journals (Sweden)

Ana M. Herrera-Navarro

2014-01-01

Full Text Available This work presents a new method for measuring the variation of intracellular calcium in follicular cells. The proposal consists in two stages: (i the detection of the cell’s nuclei and (ii the analysis of the fluorescence variations. The first stage is performed via watershed modified transformation, where the process of labeling is controlled. The detection process uses the contours of the cells as descriptors, where they are enhanced with a morphological filter that homogenizes the luminance variation of the image. In the second stage, the fluorescence variations are modeled as an exponential decreasing function, where the fluorescence variations are highly correlated with the changes of intracellular free Ca2+. Additionally, it is introduced a new morphological called medium reconstruction process, which helps to enhance the data for the modeling process. This filter exploits the undermodeling and overmodeling properties of reconstruction operators, such that it preserves the structure of the original signal. Finally, an experimental process shows evidence of the capabilities of the proposal.

Interpretation of medical images by model guided analysis

International Nuclear Information System (INIS)

Karssemeijer, N.

1989-01-01

Progress in the development of digital pictorial information systems stimulates a growing interest in the use of image analysis techniques in medicine. Especially when precise quantitative information is required the use of fast and reproducable computer analysis may be more appropriate than relying on visual judgement only. Such quantitative information can be valuable, for instance, in diagnostics or in irradiation therapy planning. As medical images are mostly recorded in a prescribed way, human anatomy guarantees a common image structure for each particular type of exam. In this thesis it is investigated how to make use of this a priori knowledge to guide image analysis. For that purpose models are developed which are suited to capture common image structure. The first part of this study is devoted to an analysis of nuclear medicine images of myocardial perfusion. In ch. 2 a model of these images is designed in order to represent characteristic image properties. It is shown that for these relatively simple images a compact symbolic description can be achieved, without significant loss of diagnostically importance of several image properties. Possibilities for automatic interpretation of more complex images is investigated in the following chapters. The central topic is segmentation of organs. Two methods are proposed and tested on a set of abdominal X-ray CT scans. Ch. 3 describes a serial approach based on a semantic network and the use of search areas. Relational constraints are used to guide the image processing and to classify detected image segments. In teh ch.'s 4 and 5 a more general parallel approach is utilized, based on a markov random field image model. A stochastic model used to represent prior knowledge about the spatial arrangement of organs is implemented as an external field. (author). 66 refs.; 27 figs.; 6 tabs

Variational multi-valued velocity field estimation for transparent sequences

DEFF Research Database (Denmark)

Ramírez-Manzanares, Alonso; Rivera, Mariano; Kornprobst, Pierre

2011-01-01

Motion estimation in sequences with transparencies is an important problem in robotics and medical imaging applications. In this work we propose a variational approach for estimating multi-valued velocity fields in transparent sequences. Starting from existing local motion estimators, we derive...... a variational model for integrating in space and time such a local information in order to obtain a robust estimation of the multi-valued velocity field. With this approach, we can indeed estimate multi-valued velocity fields which are not necessarily piecewise constant on a layer –each layer can evolve...

A Combined First and Second Order Variational Approach for Image Reconstruction

KAUST Repository

Papafitsoros, K.; Schö nlieb, C. B.

2013-01-01

the creation of undesirable artifacts and blocky-like structures in the reconstructed images-a known disadvantage of the ROF model-while being simple and efficiently numerically solvable. ©Springer Science+Business Media New York 2013.

IMAGE TO POINT CLOUD METHOD OF 3D-MODELING

Directory of Open Access Journals (Sweden)

A. G. Chibunichev

2012-07-01

Full Text Available This article describes the method of constructing 3D models of objects (buildings, monuments based on digital images and a point cloud obtained by terrestrial laser scanner. The first step is the automated determination of exterior orientation parameters of digital image. We have to find the corresponding points of the image and point cloud to provide this operation. Before the corresponding points searching quasi image of point cloud is generated. After that SIFT algorithm is applied to quasi image and real image. SIFT algorithm allows to find corresponding points. Exterior orientation parameters of image are calculated from corresponding points. The second step is construction of the vector object model. Vectorization is performed by operator of PC in an interactive mode using single image. Spatial coordinates of the model are calculated automatically by cloud points. In addition, there is automatic edge detection with interactive editing available. Edge detection is performed on point cloud and on image with subsequent identification of correct edges. Experimental studies of the method have demonstrated its efficiency in case of building facade modeling.

Joint model of motion and anatomy for PET image reconstruction

International Nuclear Information System (INIS)

Qiao Feng; Pan Tinsu; Clark, John W. Jr.; Mawlawi, Osama

2007-01-01

Anatomy-based positron emission tomography (PET) image enhancement techniques have been shown to have the potential for improving PET image quality. However, these techniques assume an accurate alignment between the anatomical and the functional images, which is not always valid when imaging the chest due to respiratory motion. In this article, we present a joint model of both motion and anatomical information by integrating a motion-incorporated PET imaging system model with an anatomy-based maximum a posteriori image reconstruction algorithm. The mismatched anatomical information due to motion can thus be effectively utilized through this joint model. A computer simulation and a phantom study were conducted to assess the efficacy of the joint model, whereby motion and anatomical information were either modeled separately or combined. The reconstructed images in each case were compared to corresponding reference images obtained using a quadratic image prior based maximum a posteriori reconstruction algorithm for quantitative accuracy. Results of these studies indicated that while modeling anatomical information or motion alone improved the PET image quantitation accuracy, a larger improvement in accuracy was achieved when using the joint model. In the computer simulation study and using similar image noise levels, the improvement in quantitation accuracy compared to the reference images was 5.3% and 19.8% when using anatomical or motion information alone, respectively, and 35.5% when using the joint model. In the phantom study, these results were 5.6%, 5.8%, and 19.8%, respectively. These results suggest that motion compensation is important in order to effectively utilize anatomical information in chest imaging using PET. The joint motion-anatomy model presented in this paper provides a promising solution to this problem

Diurnal, Seasonal, and Interannual Variations of Cloud Properties Derived for CERES From Imager Data

Science.gov (United States)

Minnis, Patrick; Young, David F.; Sun-Mack, Sunny; Trepte, Qing Z.; Chen, Yan; Brown, Richard R.; Gibson, Sharon; Heck, Patrick W.

2004-01-01

Simultaneous measurement of the radiation and cloud fields on a global basis is a key component in the effort to understand and model the interaction between clouds and radiation at the top of the atmosphere, at the surface, and within the atmosphere. The NASA Clouds and Earth s Radiant Energy System (CERES) Project, begun in 1998, is meeting this need. Broadband shortwave (SW) and longwave radiance measurements taken by the CERES scanners at resolutions between 10 and 20 km on the Tropical Rainfall Measuring Mission (TRMM), Terra, and Aqua satellites are matched to simultaneous retrievals of cloud height, phase, particle size, water path, and optical depth OD from the TRMM Visible Infrared Scanner (VIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Besides aiding the interpretation of the broadband radiances, the CERES cloud properties are valuable for understanding cloud variations at a variety of scales. In this paper, the resulting CERES cloud data taken to date are averaged at several temporal scales to examine the temporal and spatial variability of the cloud properties on a global scale at a 1 resolution.

Model-based satellite image fusion

DEFF Research Database (Denmark)

Aanæs, Henrik; Sveinsson, J. R.; Nielsen, Allan Aasbjerg

2008-01-01

A method is proposed for pixel-level satellite image fusion derived directly from a model of the imaging sensor. By design, the proposed method is spectrally consistent. It is argued that the proposed method needs regularization, as is the case for any method for this problem. A framework for pixel...... neighborhood regularization is presented. This framework enables the formulation of the regularization in a way that corresponds well with our prior assumptions of the image data. The proposed method is validated and compared with other approaches on several data sets. Lastly, the intensity......-hue-saturation method is revisited in order to gain additional insight of what implications the spectral consistency has for an image fusion method....

The effect of gender on eye colour variation in European populations and an evaluation of the IrisPlex prediction model

DEFF Research Database (Denmark)

Pietroni, Carlotta; Andersen, Jeppe D.; Johansen, Peter

2014-01-01

In two recent studies of Spanish individuals [1,2], gender was suggested as a factor that contributes to human eye colour variation. However, gender did not improve the predictive accuracy on blue, intermediate and brown eye colours when gender was included in the IrisPlex model [3]. In this stud...... and their corresponding predictive values using the IrisPlex prediction model [4]. The results suggested that maximum three (rs12913832, rs1800407, rs16891982) of the six IrisPlex SNPs are useful in practical forensic genetic casework.......In two recent studies of Spanish individuals [1,2], gender was suggested as a factor that contributes to human eye colour variation. However, gender did not improve the predictive accuracy on blue, intermediate and brown eye colours when gender was included in the IrisPlex model [3]. In this study......12203592). A quantitative eye colour score (Pixel Index of the Eye: PIE-score) was calculated based on digital eye images using the custom made DIAT software. The results were compared with those of Danish and Swedish population samples. As expected, we found HERC2 rs12913832 as the main predictor of human...

Novel Complete Probabilistic Models of Random Variation in High Frequency Performance of Nanoscale MOSFET

Directory of Open Access Journals (Sweden)

Rawid Banchuin

2013-01-01

Full Text Available The novel probabilistic models of the random variations in nanoscale MOSFET's high frequency performance defined in terms of gate capacitance and transition frequency have been proposed. As the transition frequency variation has also been considered, the proposed models are considered as complete unlike the previous one which take only the gate capacitance variation into account. The proposed models have been found to be both analytic and physical level oriented as they are the precise mathematical expressions in terms of physical parameters. Since the up-to-date model of variation in MOSFET's characteristic induced by physical level fluctuation has been used, part of the proposed models for gate capacitance is more accurate and physical level oriented than its predecessor. The proposed models have been verified based on the 65 nm CMOS technology by using the Monte-Carlo SPICE simulations of benchmark circuits and Kolmogorov-Smirnov tests as highly accurate since they fit the Monte-Carlo-based analysis results with 99% confidence. Hence, these novel models have been found to be versatile for the statistical/variability aware analysis/design of nanoscale MOSFET-based analog/mixed signal circuits and systems.

Bas-Relief Modeling from Normal Images with Intuitive Styles.

Science.gov (United States)

Ji, Zhongping; Ma, Weiyin; Sun, Xianfang

2014-05-01

Traditional 3D model-based bas-relief modeling methods are often limited to model-dependent and monotonic relief styles. This paper presents a novel method for digital bas-relief modeling with intuitive style control. Given a composite normal image, the problem discussed in this paper involves generating a discontinuity-free depth field with high compression of depth data while preserving or even enhancing fine details. In our framework, several layers of normal images are composed into a single normal image. The original normal image on each layer is usually generated from 3D models or through other techniques as described in this paper. The bas-relief style is controlled by choosing a parameter and setting a targeted height for them. Bas-relief modeling and stylization are achieved simultaneously by solving a sparse linear system. Different from previous work, our method can be used to freely design bas-reliefs in normal image space instead of in object space, which makes it possible to use any popular image editing tools for bas-relief modeling. Experiments with a wide range of 3D models and scenes show that our method can effectively generate digital bas-reliefs.

Quantitatively differentiating microstructural variations of skeletal muscle tissues by multispectral Mueller matrix imaging

Science.gov (United States)

Dong, Yang; He, Honghui; He, Chao; Ma, Hui

2016-10-01

Polarized light is sensitive to the microstructures of biological tissues and can be used to detect physiological changes. Meanwhile, spectral features of the scattered light can also provide abundant microstructural information of tissues. In this paper, we take the backscattering polarization Mueller matrix images of bovine skeletal muscle tissues during the 24-hour experimental time, and analyze their multispectral behavior using quantitative Mueller matrix parameters. In the processes of rigor mortis and proteolysis of muscle samples, multispectral frequency distribution histograms (FDHs) of the Mueller matrix elements can reveal rich qualitative structural information. In addition, we analyze the temporal variations of the sample using the multispectral Mueller matrix transformation (MMT) parameters. The experimental results indicate that the different stages of rigor mortis and proteolysis for bovine skeletal muscle samples can be judged by these MMT parameters. The results presented in this work show that combining with the multispectral technique, the FDHs and MMT parameters can characterize the microstructural variation features of skeletal muscle tissues. The techniques have the potential to be used as tools for quantitative assessment of meat qualities in food industry.

POLARIZATION IMAGING AND SCATTERING MODEL OF CANCEROUS LIVER TISSUES

Directory of Open Access Journals (Sweden)

DONGZHI LI

2013-07-01

Full Text Available We apply different polarization imaging techniques for cancerous liver tissues, and compare the relative contrasts for difference polarization imaging (DPI, degree of polarization imaging (DOPI and rotating linear polarization imaging (RLPI. Experimental results show that a number of polarization imaging parameters are capable of differentiating cancerous cells in isotropic liver tissues. To analyze the contrast mechanism of the cancer-sensitive polarization imaging parameters, we propose a scattering model containing two types of spherical scatterers and carry on Monte Carlo simulations based on this bi-component model. Both the experimental and Monte Carlo simulated results show that the RLPI technique can provide a good imaging contrast of cancerous tissues. The bi-component scattering model provides a useful tool to analyze the contrast mechanism of polarization imaging of cancerous tissues.

Statistical modeling and MAP estimation for body fat quantification with MRI ratio imaging

Science.gov (United States)

Wong, Wilbur C. K.; Johnson, David H.; Wilson, David L.

2008-03-01

We are developing small animal imaging techniques to characterize the kinetics of lipid accumulation/reduction of fat depots in response to genetic/dietary factors associated with obesity and metabolic syndromes. Recently, we developed an MR ratio imaging technique that approximately yields lipid/{lipid + water}. In this work, we develop a statistical model for the ratio distribution that explicitly includes a partial volume (PV) fraction of fat and a mixture of a Rician and multiple Gaussians. Monte Carlo hypothesis testing showed that our model was valid over a wide range of coefficient of variation of the denominator distribution (c.v.: 0-0:20) and correlation coefficient among the numerator and denominator (ρ 0-0.95), which cover the typical values that we found in MRI data sets (c.v.: 0:027-0:063, ρ: 0:50-0:75). Then a maximum a posteriori (MAP) estimate for the fat percentage per voxel is proposed. Using a digital phantom with many PV voxels, we found that ratio values were not linearly related to PV fat content and that our method accurately described the histogram. In addition, the new method estimated the ground truth within +1.6% vs. +43% for an approach using an uncorrected ratio image, when we simply threshold the ratio image. On the six genetically obese rat data sets, the MAP estimate gave total fat volumes of 279 +/- 45mL, values 21% smaller than those from the uncorrected ratio images, principally due to the non-linear PV effect. We conclude that our algorithm can increase the accuracy of fat volume quantification even in regions having many PV voxels, e.g. ectopic fat depots.

Long-Term Evaluation of Ocean Tidal Variation Models of Polar Motion and UT1

Science.gov (United States)

Karbon, Maria; Balidakis, Kyriakos; Belda, Santiago; Nilsson, Tobias; Hagedoorn, Jan; Schuh, Harald

2018-04-01

Recent improvements in the development of VLBI (very long baseline interferometry) and other space geodetic techniques such as the global navigation satellite systems (GNSS) require very precise a-priori information of short-period (daily and sub-daily) Earth rotation variations. One significant contribution to Earth rotation is caused by the diurnal and semi-diurnal ocean tides. Within this work, we developed a new model for the short-period ocean tidal variations in Earth rotation, where the ocean tidal angular momentum model and the Earth rotation variation have been setup jointly. Besides the model of the short-period variation of the Earth's rotation parameters (ERP), based on the empirical ocean tide model EOT11a, we developed also ERP models, that are based on the hydrodynamic ocean tide models FES2012 and HAMTIDE. Furthermore, we have assessed the effect of uncertainties in the elastic Earth model on the resulting ERP models. Our proposed alternative ERP model to the IERS 2010 conventional model considers the elastic model PREM and 260 partial tides. The choice of the ocean tide model and the determination of the tidal velocities have been identified as the main uncertainties. However, in the VLBI analysis all models perform on the same level of accuracy. From these findings, we conclude that the models presented here, which are based on a re-examined theoretical description and long-term satellite altimetry observation only, are an alternative for the IERS conventional model but do not improve the geodetic results.

Temporal variation and scaling of parameters for a monthly hydrologic model

Science.gov (United States)

Deng, Chao; Liu, Pan; Wang, Dingbao; Wang, Weiguang

2018-03-01

The temporal variation of model parameters is affected by the catchment conditions and has a significant impact on hydrological simulation. This study aims to evaluate the seasonality and downscaling of model parameter across time scales based on monthly and mean annual water balance models with a common model framework. Two parameters of the monthly model, i.e., k and m, are assumed to be time-variant at different months. Based on the hydrological data set from 121 MOPEX catchments in the United States, we firstly analyzed the correlation between parameters (k and m) and catchment properties (NDVI and frequency of rainfall events, α). The results show that parameter k is positively correlated with NDVI or α, while the correlation is opposite for parameter m, indicating that precipitation and vegetation affect monthly water balance by controlling temporal variation of parameters k and m. The multiple linear regression is then used to fit the relationship between ε and the means and coefficient of variations of parameters k and m. Based on the empirical equation and the correlations between the time-variant parameters and NDVI, the mean annual parameter ε is downscaled to monthly k and m. The results show that it has lower NSEs than these from model with time-variant k and m being calibrated through SCE-UA, while for several study catchments, it has higher NSEs than that of the model with constant parameters. The proposed method is feasible and provides a useful tool for temporal scaling of model parameter.

Analysis of scene distortions in stereoscopic images due to the variation of the ideal viewing conditions

Science.gov (United States)

Viale, Alberto; Villa, Dario

2011-03-01

Recently stereoscopy has increased a lot its popularity and various technologies are spreading in theaters and homes allowing observation of stereoscopic images and movies, becoming affordable even for home users. However there are some golden rules that users should follow to ensure a better enjoyment of stereoscopic images, first of all the viewing condition should not be too different from the ideal ones, which were assumed during the production process. To allow the user to perceive stereo depth instead of a flat image, two different views of the same scene are shown to the subject, one is seen just through his left eye and the other just through the right one; the vision process is making the work of merging the two images in a virtual three-dimensional scene, giving to the user the perception of depth. The two images presented to the user were created, either from image synthesis or from more traditional techniques, following the rules of perspective. These rules need some boundary conditions to be explicit, such as eye separation, field of view, parallax distance, viewer position and orientation. In this paper we are interested in studying how the variation of the viewer position and orientation from the ideal ones expressed as specified parameters in the image creation process, is affecting the correctness of the reconstruction of the three-dimensional virtual scene.

Exploring gravitational lensing model variations in the Frontier Fields galaxy clusters

Science.gov (United States)

Harris James, Nicholas John; Raney, Catie; Brennan, Sean; Keeton, Charles

2018-01-01

Multiple groups have been working on modeling the mass distributions of the six lensing galaxy clusters in the Hubble Space Telescope Frontier Fields data set. The magnification maps produced from these mass models will be important for the future study of the lensed background galaxies, but there exists significant variation in the different groups’ models and magnification maps. We explore the use of two-dimensional histograms as a tool for visualizing these magnification map variations. Using a number of simple, one- or two-halo singular isothermal sphere models, we explore the features that are produced in 2D histogram model comparisons when parameters such as halo mass, ellipticity, and location are allowed to vary. Our analysis demonstrates the potential of 2D histograms as a means of observing the full range of differences between the Frontier Fields groups’ models.This work has been supported by funding from National Science Foundation grants PHY-1560077 and AST-1211385, and from the Space Telescope Science Institute.

A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

Directory of Open Access Journals (Sweden)

Ying Cai

2012-09-01

Full Text Available In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT, the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3% and overall (92.0%–93.1% accuracies. Our

GPR Imaging for Deeply Buried Objects: A Comparative Study Based on FDTD Models and Field Experiments

Science.gov (United States)

Tilley, roger; Dowla, Farid; Nekoogar, Faranak; Sadjadpour, Hamid

2012-01-01

Conventional use of Ground Penetrating Radar (GPR) is hampered by variations in background environmental conditions, such as water content in soil, resulting in poor repeatability of results over long periods of time when the radar pulse characteristics are kept the same. Target objects types might include voids, tunnels, unexploded ordinance, etc. The long-term objective of this work is to develop methods that would extend the use of GPR under various environmental and soil conditions provided an optimal set of radar parameters (such as frequency, bandwidth, and sensor configuration) are adaptively employed based on the ground conditions. Towards that objective, developing Finite Difference Time Domain (FDTD) GPR models, verified by experimental results, would allow us to develop analytical and experimental techniques to control radar parameters to obtain consistent GPR images with changing ground conditions. Reported here is an attempt at developing 20 and 3D FDTD models of buried targets verified by two different radar systems capable of operating over different soil conditions. Experimental radar data employed were from a custom designed high-frequency (200 MHz) multi-static sensor platform capable of producing 3-D images, and longer wavelength (25 MHz) COTS radar (Pulse EKKO 100) capable of producing 2-D images. Our results indicate different types of radar can produce consistent images.

Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

KAUST Repository

Reyes, Juan Carlos De los

2013-11-01

We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

KAUST Repository

Reyes, Juan Carlos De los; Schö nlieb, Carola-Bibiane

2013-01-01

We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

Modelling the quiet-time geomagnetic daily variations using observatory data

OpenAIRE

Hamilton, Brian; Macmillan, Susan

2008-01-01

We present on-going work towards building a global model of the quiet-time geomagnetic daily variation using bservatory data. We select hourly mean data during June 2006 (solar minimum). We fit Fourier series in time, with a fundamental period of 24 hours, to the data at each observatory. We then use global spherical harmonic expansions to separate the daily variation signal, as characterised by the Fourier coefficients in time, into external and induced internal contributions. The mode...

Nonparametric Mixture Models for Supervised Image Parcellation.

Science.gov (United States)

Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

2009-09-01

We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.

Common-image gathers in the offset domain from reverse-time migration

KAUST Repository

Zhan, Ge; Zhang, Minyu

2014-01-01

Kirchhoff migration is flexible to output common-image gathers (CIGs) in the offset domain by imaging data with different offsets separately. These CIGs supply important information for velocity model updates and amplitude-variation-with-offset (AVO

Normal variation and long-term reproducibility of image-selected in vivo brain MR spectroscopy

International Nuclear Information System (INIS)

Smith, M.A.; Porter, D.; Lowry, M.; Ayton, V.; Twelves, C.J.; Richards, M.A.; Garlick, P.; Maisey, M.N.

1988-01-01

MR spectroscopy of P-31 in the brain was performed with a 1.5-T MR imaging and spectroscopy system using ISIS with a 5-cm cube. A standardized spectral processing routine was adopted, and the ratios of peak areas were measured. Localized brain spectra were obtained from 17 healthy subjects, of whom ten had undergone repeated investigations after a delay of at least 1 month. The variation among healthy subjects, expressed as the mean +- standard deviation, and the long-term reproducibility, expressed as the coefficient of variation, were as follows: for peak areas phosphocreatine (PCr) Pi 2.46 +- 0.72, 21.3%, for PCr/PME, 1.97 +- 0.62, 16.8%, for PCr/PDE, 0.51 +- 0.07, 8.1%; for PCr/Υ-adenosine triphosphate (ATP), 1.13 + 0.15, 6.3%; for PCr/α-ATP, 1.09 +- 0.21, 10.3%, for PCr/β-ATP, 1.66 +- .027, 10.4%; and for pH, 7.00 +- 0.05, 0.8%

A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images

Science.gov (United States)

McClelland, Jamie R.; Modat, Marc; Arridge, Simon; Grimes, Helen; D'Souza, Derek; Thomas, David; O' Connell, Dylan; Low, Daniel A.; Kaza, Evangelia; Collins, David J.; Leach, Martin O.; Hawkes, David J.

2017-06-01

Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of ‘partial’ imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated.

Does litter size variation affect models of terrestrial carnivore extinction risk and management?

Directory of Open Access Journals (Sweden)

Eleanor S Devenish-Nelson

Full Text Available Individual variation in both survival and reproduction has the potential to influence extinction risk. Especially for rare or threatened species, reliable population models should adequately incorporate demographic uncertainty. Here, we focus on an important form of demographic stochasticity: variation in litter sizes. We use terrestrial carnivores as an example taxon, as they are frequently threatened or of economic importance. Since data on intraspecific litter size variation are often sparse, it is unclear what probability distribution should be used to describe the pattern of litter size variation for multiparous carnivores.We used litter size data on 32 terrestrial carnivore species to test the fit of 12 probability distributions. The influence of these distributions on quasi-extinction probabilities and the probability of successful disease control was then examined for three canid species - the island fox Urocyon littoralis, the red fox Vulpes vulpes, and the African wild dog Lycaon pictus. Best fitting probability distributions differed among the carnivores examined. However, the discretised normal distribution provided the best fit for the majority of species, because variation among litter-sizes was often small. Importantly, however, the outcomes of demographic models were generally robust to the distribution used.These results provide reassurance for those using demographic modelling for the management of less studied carnivores in which litter size variation is estimated using data from species with similar reproductive attributes.

Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

Science.gov (United States)

Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

2017-09-01

Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

Analysis and Comprehensive Analytical Modeling of Statistical Variations in Subthreshold MOSFET's High Frequency Characteristics

Directory of Open Access Journals (Sweden)

Rawid Banchuin

2014-01-01

Full Text Available In this research, the analysis of statistical variations in subthreshold MOSFET's high frequency characteristics defined in terms of gate capacitance and transition frequency, have been shown and the resulting comprehensive analytical models of such variations in terms of their variances have been proposed. Major imperfection in the physical level properties including random dopant fluctuation and effects of variations in MOSFET's manufacturing process, have been taken into account in the proposed analysis and modeling. The up to dated comprehensive analytical model of statistical variation in MOSFET's parameter has been used as the basis of analysis and modeling. The resulting models have been found to be both analytic and comprehensive as they are the precise mathematical expressions in terms of physical level variables of MOSFET. Furthermore, they have been verified at the nanometer level by using 65~nm level BSIM4 based benchmarks and have been found to be very accurate with smaller than 5 % average percentages of errors. Hence, the performed analysis gives the resulting models which have been found to be the potential mathematical tool for the statistical and variability aware analysis and design of subthreshold MOSFET based VHF circuits, systems and applications.

Modeling human faces with multi-image photogrammetry

Science.gov (United States)

D'Apuzzo, Nicola

2002-03-01

Modeling and measurement of the human face have been increasing by importance for various purposes. Laser scanning, coded light range digitizers, image-based approaches and digital stereo photogrammetry are the used methods currently employed in medical applications, computer animation, video surveillance, teleconferencing and virtual reality to produce three dimensional computer models of the human face. Depending on the application, different are the requirements. Ours are primarily high accuracy of the measurement and automation in the process. The method presented in this paper is based on multi-image photogrammetry. The equipment, the method and results achieved with this technique are here depicted. The process is composed of five steps: acquisition of multi-images, calibration of the system, establishment of corresponding points in the images, computation of their 3-D coordinates and generation of a surface model. The images captured by five CCD cameras arranged in front of the subject are digitized by a frame grabber. The complete system is calibrated using a reference object with coded target points, which can be measured fully automatically. To facilitate the establishment of correspondences in the images, texture in the form of random patterns can be projected from two directions onto the face. The multi-image matching process, based on a geometrical constrained least squares matching algorithm, produces a dense set of corresponding points in the five images. Neighborhood filters are then applied on the matching results to remove the errors. After filtering the data, the three-dimensional coordinates of the matched points are computed by forward intersection using the results of the calibration process; the achieved mean accuracy is about 0.2 mm in the sagittal direction and about 0.1 mm in the lateral direction. The last step of data processing is the generation of a surface model from the point cloud and the application of smooth filters. Moreover, a

Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

Science.gov (United States)

Abellán-Nebot, J. V.; Liu, J.; Romero, F.

2009-11-01

The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

Image-Based 3D Face Modeling System

Directory of Open Access Journals (Sweden)

Vladimir Vezhnevets

2005-08-01

Full Text Available This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose, mouth, and ear. The shape deformation module utilizes the detected features to deform the generic head mesh model such that the deformed model coincides with the detected features. A texture is created by combining the facial textures augmented from the input images and the synthesized texture and mapped onto the deformed generic head model. This paper provides a practical system for 3D face modeling, which is highly automated by aggregating, customizing, and optimizing a bunch of individual computer vision algorithms. The experimental results show a highly automated process of modeling, which is sufficiently robust to various imaging conditions. The whole model creation including all the optional manual corrections takes only 2∼3 minutes.

Variational approach to thermal masses in compactified models

Energy Technology Data Exchange (ETDEWEB)

Dominici, Daniele [Dipartimento di Fisica e Astronomia Università di Firenze and INFN - Sezione di Firenze,Via G. Sansone 1, 50019 Sesto Fiorentino (Italy); Roditi, Itzhak [Centro Brasileiro de Pesquisas Físicas - CBPF/MCT,Rua Dr. Xavier Sigaud 150, 22290-180, Rio de Janeiro, RJ (Brazil)

2015-08-20

We investigate by means of a variational approach the effective potential of a 5DU(1) scalar model at finite temperature and compactified on S{sup 1} and S{sup 1}/Z{sub 2} as well as the corresponding 4D model obtained through a trivial dimensional reduction. We are particularly interested in the behavior of the thermal masses of the scalar field with respect to the Wilson line phase and the results obtained are compared with those coming from a one-loop effective potential calculation. We also explore the nature of the phase transition.

pyBSM: A Python package for modeling imaging systems

Science.gov (United States)

LeMaster, Daniel A.; Eismann, Michael T.

2017-05-01

There are components that are common to all electro-optical and infrared imaging system performance models. The purpose of the Python Based Sensor Model (pyBSM) is to provide open source access to these functions for other researchers to build upon. Specifically, pyBSM implements much of the capability found in the ERIM Image Based Sensor Model (IBSM) V2.0 along with some improvements. The paper also includes two use-case examples. First, performance of an airborne imaging system is modeled using the General Image Quality Equation (GIQE). The results are then decomposed into factors affecting noise and resolution. Second, pyBSM is paired with openCV to evaluate performance of an algorithm used to detect objects in an image.

Assessment of the impact of modeling axial compression on PET image reconstruction.

Science.gov (United States)

Belzunce, Martin A; Reader, Andrew J

2017-10-01

frequencies. Modeling the axial compression also achieved a lower coefficient of variation but with an increase of intervoxel correlations. The unmatched projector/backprojector achieved similar contrast values to the matched version at considerably lower reconstruction times, but at the cost of noisier images. For a line source scan, the reconstructions with modeling of the axial compression achieved similar resolution to the span 1 reconstructions. Axial compression applied to PET sinograms was found to have a negligible impact for span values lower than 7. For span values up to 21, the spatial resolution degradation due to the axial compression can be almost completely compensated for by modeling this effect in the system matrix at the expense of considerably larger processing times and higher intervoxel correlations, while retaining the storage benefit of compressed data. For even higher span values, the resolution loss cannot be completely compensated possibly due to an effective null space in the system. The use of an unmatched projector/backprojector proved to be a practical solution to compensate for the spatial resolution degradation at a reasonable computational cost but can lead to noisier images. © 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

New Approaches For Asteroid Spin State and Shape Modeling From Delay-Doppler Radar Images

Science.gov (United States)

Raissi, Chedy; Lamee, Mehdi; Mosiane, Olorato; Vassallo, Corinne; Busch, Michael W.; Greenberg, Adam; Benner, Lance A. M.; Naidu, Shantanu P.; Duong, Nicholas

2016-10-01

Delay-Doppler radar imaging is a powerful technique to characterize the trajectories, shapes, and spin states of near-Earth asteroids; and has yielded detailed models of dozens of objects. Reconstructing objects' shapes and spins from delay-Doppler data is a computationally intensive inversion problem. Since the 1990s, delay-Doppler data has been analyzed using the SHAPE software. SHAPE performs sequential single-parameter fitting, and requires considerable computer runtime and human intervention (Hudson 1993, Magri et al. 2007). Recently, multiple-parameter fitting algorithms have been shown to more efficiently invert delay-Doppler datasets (Greenberg & Margot 2015) - decreasing runtime while improving accuracy. However, extensive human oversight of the shape modeling process is still required. We have explored two new techniques to better automate delay-Doppler shape modeling: Bayesian optimization and a machine-learning neural network.One of the most time-intensive steps of the shape modeling process is to perform a grid search to constrain the target's spin state. We have implemented a Bayesian optimization routine that uses SHAPE to autonomously search the space of spin-state parameters. To test the efficacy of this technique, we compared it to results with human-guided SHAPE for asteroids 1992 UY4, 2000 RS11, and 2008 EV5. Bayesian optimization yielded similar spin state constraints within a factor of 3 less computer runtime.The shape modeling process could be further accelerated using a deep neural network to replace iterative fitting. We have implemented a neural network with a variational autoencoder (VAE), using a subset of known asteroid shapes and a large set of synthetic radar images as inputs to train the network. Conditioning the VAE in this manner allows the user to give the network a set of radar images and get a 3D shape model as an output. Additional development will be required to train a network to reliably render shapes from delay

A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images.

Science.gov (United States)

Wang, Ping; Xu, Wengui; Sun, Jian; Yang, Chengwen; Wang, Gang; Sa, Yu; Hu, Xin-Hua; Feng, Yuanming

2016-01-01

It has been shown that the intratumor heterogeneity can be characterized with quantitative analysis of the [18]F-FDG PET image data. The existing models employ multiple parameters for feature extraction which makes it difficult to implement in clinical settings for the quantitative characterization. This article reports an easy-to-use and differential SUV based model for quantitative assessment of the intratumor heterogeneity from 3D [18]F-FDG PET image data. An H index is defined to assess tumor heterogeneity by summing voxel-wise distribution of differential SUV from the [18]F-FDG PET image data. The summation is weighted by the distance of SUV difference among neighboring voxels from the center of the tumor and can thus yield increased values for tumors with peripheral sub-regions of high SUV that often serves as an indicator of augmented malignancy. Furthermore, the sign of H index is used to differentiate the rate of change for volume averaged SUV from its center to periphery. The new model with the H index has been compared with a widely-used model of gray level co-occurrence matrix (GLCM) for image texture characterization with phantoms of different configurations and the [18]F-FDG PET image data of 6 lung cancer patients to evaluate its effectiveness and feasibility for clinical uses. The comparison of the H index and GLCM parameters with the phantoms demonstrate that the H index can characterize the SUV heterogeneity in all of 6 2D phantoms while only 1 GLCM parameter can do for 1 and fail to differentiate for other 2D phantoms. For the 8 3D phantoms, the H index can clearly differentiate all of them while the 4 GLCM parameters provide complicated patterns in the characterization. Feasibility study with the PET image data from 6 lung cancer patients show that the H index provides an effective single-parameter metric to characterize tumor heterogeneity in terms of the local SUV variation, and it has higher correlation with tumor volume change after

Ultrasonic-assisted manufacturing processes: Variational model and numerical simulations

KAUST Repository

Siddiq, Amir

2012-04-01

We present a computational study of ultrasonic assisted manufacturing processes including sheet metal forming, upsetting, and wire drawing. A fully variational porous plasticity model is modified to include ultrasonic softening effects and then utilized to account for instantaneous softening when ultrasonic energy is applied during deformation. Material model parameters are identified via inverse modeling, i.e. by using experimental data. The versatility and predictive ability of the model are demonstrated and the effect of ultrasonic intensity on the manufacturing process at hand is investigated and compared qualitatively with experimental results reported in the literature. © 2011 Elsevier B.V. All rights reserved.

Single image interpolation via adaptive nonlocal sparsity-based modeling.

Science.gov (United States)

Romano, Yaniv; Protter, Matan; Elad, Michael

2014-07-01

Single image interpolation is a central and extensively studied problem in image processing. A common approach toward the treatment of this problem in recent years is to divide the given image into overlapping patches and process each of them based on a model for natural image patches. Adaptive sparse representation modeling is one such promising image prior, which has been shown to be powerful in filling-in missing pixels in an image. Another force that such algorithms may use is the self-similarity that exists within natural images. Processing groups of related patches together exploits their correspondence, leading often times to improved results. In this paper, we propose a novel image interpolation method, which combines these two forces-nonlocal self-similarities and sparse representation modeling. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve state-of-the-art results.

Remote Sensing Image Registration Using Multiple Image Features

Directory of Open Access Journals (Sweden)

Kun Yang

2017-06-01

Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Exploring The Limits Of Variational Passive Microwave Retrievals

Science.gov (United States)

Duncan, David Ian

Passive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval

Accuracy of the Heidelberg Spectralis in the alignment between near-infrared image and tomographic scan in a model eye: a multicenter study.

Science.gov (United States)

Barteselli, Giulio; Bartsch, Dirk-Uwe; Viola, Francesco; Mojana, Francesca; Pellegrini, Marco; Hartmann, Kathrin I; Benatti, Eleonora; Leicht, Simon; Ratiglia, Roberto; Staurenghi, Giovanni; Weinreb, Robert N; Freeman, William R

2013-09-01

To evaluate temporal changes and predictors of accuracy in the alignment between simultaneous near-infrared image and optical coherence tomography (OCT) scan on the Heidelberg Spectralis using a model eye. Laboratory investigation. After calibrating the device, 6 sites performed weekly testing of the alignment for 12 weeks using a model eye. The maximum error was compared with multiple variables to evaluate predictors of inaccurate alignment. Variables included the number of weekly scanned patients, total number of OCT scans and B-scans performed, room temperature and its variation, and working time of the scanning laser. A 4-week extension study was subsequently performed to analyze short-term changes in the alignment. The average maximum error in the alignment was 15 ± 6 μm; the greatest error was 35 μm. The error increased significantly at week 1 (P = .01), specifically after the second imaging study (P alignment were temperature variation and scans per patient (P imaging study. To improve the accuracy, room temperature should be kept stable and unnecessary scans should be avoided. The alignment of the device does not need to be checked on a regular basis in the clinical setting, but it should be checked after every other patient for more precise research purposes. Published by Elsevier Inc.

Image guided radiotherapy: equipment specifications and performance - an analysis of the dosimetric consequences of anatomic variations during head-and-neck radiotherapy treatment

International Nuclear Information System (INIS)

Marguet, Maud

2009-01-01

Anatomic variations during head-and-neck radiotherapy treatment may compromise the delivery of the planned dose distribution, particularly in the case of IMRT treatments. The aim of this thesis was to establish 'dosimetric indicators' to identify patients who delivered dose deviates from the planned dose, to allow an eventual re-optimisation of the patient's dosimetry, if necessary, during the course of their radiotherapy treatment. These anatomic variations were monitored by regular acquisition of 3D patient images using an onboard imaging system, for which a rigorous quality control program was implemented. The patient dose distribution analysis and comparison was performed using a modified gamma index technique which was named gammaLSC3D. This improved gamma index technique quantified and identified the location of changes in the dose distribution in a stack of 2D images, with particular reference to the target volume (PTV) or organs at risk (parotids). The changes observed in the dose distribution for the PTV or parotids were then analysed and presented in the form of gamma-volume histograms in order to facilitate the follow up of dosimetric changes during the radiotherapy treatment. This analysis method has been automated, and is applicable in clinical routine to follow dose variations during head and neck radiotherapy treatment. (author) [fr

Analysis of spin and gauge models with variational methods

International Nuclear Information System (INIS)

Dagotto, E.; Masperi, L.; Moreo, A.; Della Selva, A.; Fiore, R.

1985-01-01

Since independent-site (link) or independent-link (plaquette) variational states enhance the order or the disorder, respectively, in the treatment of spin (gauge) models, we prove that mixed states are able to improve the critical coupling while giving the qualitatively correct behavior of the relevant parameters

Cardiovascular Imaging: What Have We Learned From Animal Models?

Directory of Open Access Journals (Sweden)

Arnoldo eSantos

2015-10-01

Full Text Available Cardiovascular imaging has become an indispensable tool for patient diagnosis and follow up. Probably the wide clinical applications of imaging are due to the possibility of a detailed and high quality description and quantification of cardiovascular system structure and function. Also phenomena that involve complex physiological mechanisms and biochemical pathways, such as inflammation and ischemia, can be visualized in a nondestructive way. The widespread use and evolution of imaging would not have been possible without animal studies. Animal models have allowed for instance, i the technical development of different imaging tools, ii to test hypothesis generated from human studies and finally, iii to evaluate the translational relevance assessment of in vitro and ex-vivo results. In this review, we will critically describe the contribution of animal models to the use of biomedical imaging in cardiovascular medicine. We will discuss the characteristics of the most frequent models used in/for imaging studies. We will cover the major findings of animal studies focused in the cardiovascular use of the repeatedly used imaging techniques in clinical practice and experimental studies. We will also describe the physiological findings and/or learning processes for imaging applications coming from models of the most common cardiovascular diseases. In these diseases, imaging research using animals has allowed the study of aspects such as: ventricular size, shape, global function and wall thickening, local myocardial function, myocardial perfusion, metabolism and energetic assessment, infarct quantification, vascular lesion characterization, myocardial fiber structure, and myocardial calcium uptake. Finally we will discuss the limitations and future of imaging research with animal models.

Glomus Tumors: Symptom Variations and Magnetic Resonance Imaging for Diagnosis

Directory of Open Access Journals (Sweden)

Ki Weon Ham

2013-07-01

Full Text Available Background The typical clinical symptoms of glomus tumors are pain, tenderness, and sensitivity to temperature change, and the presence of these clinical findings is helpful in diagnosis. However, the tumors often pose diagnostic difficulty because of variations in presentation and the nonspecific symptoms of glomus tumors. To the best of our knowledge, few studies have reported on the usefulness of magnetic resonance imaging (MRI in diagnosing glomus tumors in patients with unspecific symptoms.Methods The inclusion criteria of this study were: having undergone surgery for subungual glomus tumor of the hand, histopathologic confirmation of glomus tumor, and having undergone preoperative MRI. Twenty-one patients were enrolled. The characteristics of the tumors and the presenting symptoms including pain, tenderness, and sensitivity to temperature change were retrospectively reviewed.Results Five out of 21 patients (23% did not show the typical glomus tumor symptom triad because they did not complain of pain provoked by coldness. Nevertheless, preoperative MRI showed well-defined small soft-tissue lesions on T1- and T2-weighted images, which are typical findings of glomus tumors. The tumors were completely resected and confirmed as glomus tumor histopathologically.Conclusions Early occult lesions of glomus tumor in the hand may not be revealed by physical examination because of their barely detectable symptoms. Moreover, subungual lesions may be particularly difficult to evaluate on physical examination. Our cases showed that MRI offers excellent diagnostic information in clinically undiagnosed or misdiagnosed patients. Preoperative MRI can accurately define the character and extent of glomus tumor, even though it is impalpable and invisible.

Glomus Tumors: Symptom Variations and Magnetic Resonance Imaging for Diagnosis

Directory of Open Access Journals (Sweden)

Ki Weon Ham

2013-07-01

Full Text Available BackgroundThe typical clinical symptoms of glomus tumors are pain, tenderness, and sensitivity to temperature change, and the presence of these clinical findings is helpful in diagnosis. However, the tumors often pose diagnostic difficulty because of variations in presentation and the nonspecific symptoms of glomus tumors. To the best of our knowledge, few studies have reported on the usefulness of magnetic resonance imaging (MRI in diagnosing glomus tumors in patients with unspecific symptoms.MethodsThe inclusion criteria of this study were: having undergone surgery for subungual glomus tumor of the hand, histopathologic confirmation of glomus tumor, and having undergone preoperative MRI. Twenty-one patients were enrolled. The characteristics of the tumors and the presenting symptoms including pain, tenderness, and sensitivity to temperature change were retrospectively reviewed.ResultsFive out of 21 patients (23% did not show the typical glomus tumor symptom triad because they did not complain of pain provoked by coldness. Nevertheless, preoperative MRI showed well-defined small soft-tissue lesions on T1- and T2-weighted images, which are typical findings of glomus tumors. The tumors were completely resected and confirmed as glomus tumor histopathologically.ConclusionsEarly occult lesions of glomus tumor in the hand may not be revealed by physical examination because of their barely detectable symptoms. Moreover, subungual lesions may be particularly difficult to evaluate on physical examination. Our cases showed that MRI offers excellent diagnostic information in clinically undiagnosed or misdiagnosed patients. Preoperative MRI can accurately define the character and extent of glomus tumor, even though it is impalpable and invisible.

Image/video understanding systems based on network-symbolic models

Science.gov (United States)

Kuvich, Gary

2004-03-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. Computer simulation models are built on the basis of graphs/networks. The ability of human brain to emulate similar graph/network models is found. Symbols, predicates and grammars naturally emerge in such networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type relational structure created via multilevel hierarchical compression of visual information. Primary areas provide active fusion of image features on a spatial grid-like structure, where nodes are cortical columns. Spatial logic and topology naturally present in such structures. Mid-level vision processes like perceptual grouping, separation of figure from ground, are special kinds of network transformations. They convert primary image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models combines learning, classification, and analogy together with higher-level model-based reasoning into a single framework, and it works similar to frames and agents. Computational intelligence methods transform images into model-based knowledge representation. Based on such principles, an Image/Video Understanding system can convert images into the knowledge models, and resolve uncertainty and ambiguity. This allows creating intelligent computer vision systems for design and manufacturing.

SIMULATING THE TIMESCALE-DEPENDENT COLOR VARIATION IN QUASARS WITH A REVISED INHOMOGENEOUS DISK MODEL

Energy Technology Data Exchange (ETDEWEB)

Cai, Zhen-Yi; Wang, Jun-Xian; Sun, Yu-Han; Wu, Mao-Chun; Huang, Xing-Xing; Chen, Xiao-Yang [CAS Key Laboratory for Researches in Galaxies and Cosmology, University of Science and Technology of China, Chinese Academy of Sciences, Hefei, Anhui 230026 (China); Gu, Wei-Min, E-mail: zcai@ustc.edu.cn, E-mail: jxw@ustc.edu.cn [Department of Astronomy and Institute of Theoretical Physics and Astrophysics, Xiamen University, Xiamen, Fujian 361005 (China)

2016-07-20

The UV–optical variability of active galactic nuclei and quasars is useful for understanding the physics of the accretion disk and is gradually being attributed to stochastic fluctuations over the accretion disk. Quasars generally appear bluer when they brighten in the UV–optical bands; the nature of this phenomenon remains controversial. Recently, Sun et al. discovered that the color variation of quasars is timescale-dependent, in the way that faster variations are even bluer than longer term ones. While this discovery can directly rule out models that simply attribute the color variation to contamination from the host galaxies, or to changes in the global accretion rates, it favors the stochastic disk fluctuation model as fluctuations in the inner-most hotter disk could dominate the short-term variations. In this work, we show that a revised inhomogeneous disk model, where the characteristic timescales of thermal fluctuations in the disk are radius-dependent (i.e., τ ∼ r ; based on that originally proposed by Dexter and Agol), can reproduce well a timescale-dependent color variation pattern, similar to the observed one and unaffected by the uneven sampling and photometric error. This demonstrates that one may statistically use variation emission at different timescales to spatially resolve the accretion disk in quasars, thus opening a new window with which to probe and test the accretion disk physics in the era of time domain astronomy. Caveats of the current model, which ought to be addressed in future simulations, are discussed.

Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations.

Science.gov (United States)

Stoll, Markus; Stoiber, Eva Maria; Grimm, Sarah; Debus, Jürgen; Bendl, Rolf; Giske, Kristina

2016-01-01

Intensity modulated radiation therapy (IMRT) of head and neck tumors allows a precise conformation of the high-dose region to clinical target volumes (CTVs) while respecting dose limits to organs a risk (OARs). Accurate patient setup reduces translational and rotational deviations between therapy planning and therapy delivery days. However, uncertainties in the shape of the CTV and OARs due to e.g. small pose variations in the highly deformable anatomy of the head and neck region can still compromise the dose conformation. Routinely applied safety margins around the CTV cause higher dose deposition in adjacent healthy tissue and should be kept as small as possible. In this work we evaluate and compare three approaches for margin generation 1) a clinically used approach with a constant isotropic 3 mm margin, 2) a previously proposed approach adopting a spatial model of the patient and 3) a newly developed approach adopting a biomechanical model of the patient. All approaches are retrospectively evaluated using a large patient cohort of over 500 fraction control CT images with heterogeneous pose changes. Automatic methods for finding landmark positions in the control CT images are combined with a patient specific biomechanical finite element model to evaluate the CTV deformation. The applied methods for deformation modeling show that the pose changes cause deformations in the target region with a mean motion magnitude of 1.80 mm. We found that the CTV size can be reduced by both variable margin approaches by 15.6% and 13.3% respectively, while maintaining the CTV coverage. With approach 3 an increase of target coverage was obtained. Variable margins increase target coverage, reduce risk to OARs and improve healthy tissue sparing at the same time.

Image-Based Models Using Crowdsourcing Strategy

Directory of Open Access Journals (Sweden)

Antonia Spanò

2016-12-01

Full Text Available The conservation and valorization of Cultural Heritage require an extensive documentation, both in properly historic-artistic terms and regarding the physical characteristics of position, shape, color, and geometry. With the use of digital photogrammetry that make acquisition of overlapping images for 3D photo modeling and with the development of dense and accurate 3D point models, it is possible to obtain high-resolution orthoprojections of surfaces.Recent years have seen a growing interest in crowdsourcing that holds in the field of the protection and dissemination of cultural heritage, in parallel there is an increasing awareness for contributing the generation of digital models with the immense wealth of images available on the web which are useful for documentation heritage.In this way, the availability and ease the automation of SfM (Structure from Motion algorithm enables the generation of digital models of the built heritage, which can be inserted positively in crowdsourcing processes. In fact, non-expert users can handle the technology in the process of acquisition, which today is one of the fundamental points to involve the wider public to the cultural heritage protection. To present the image based models and their derivatives that can be made from a great digital resource; the current approach is useful for the little-known heritage or not easily accessible buildings as an emblematic case study that was selected. It is the Vank Cathedral in Isfahan in Iran: the availability of accurate point clouds and reliable orthophotos are very convenient since the building of the Safavid epoch (cent. XVII-XVIII completely frescoed with the internal surfaces, which the architecture and especially the architectural decoration reach their peak.The experimental part of the paper explores also some aspects of usability of the digital output from the image based modeling methods. The availability of orthophotos allows and facilitates the iconographic

Variational solution of the Gross-Neveu model; 2, finite-N and renormalization

CERN Document Server

Arvanitis, C; Iacomi, M; Kneur, J L; Neveu, A

1995-01-01

We show how to perform systematically improvable variational calculations in the O(2N) Gross-Neveu model for generic N, in such a way that all infinities usually plaguing such calculations are accounted for in a way compatible with the renormalization group. The final point is a general framework for the calculation of non-perturbative quantities like condensates, masses, etc..., in an asymptotically free field theory. For the Gross-Neveu model, the numerical results obtained from a "two-loop" variational calculation are in very good agreement with exact quantities down to low values of N.

SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients

Energy Technology Data Exchange (ETDEWEB)

Dhou, S; Williams, C [Brigham and Women’s Hospital / Harvard Medical School, Boston, MA (United States); Ionascu, D [William Beaumont Hospital, Royal Oak, MI (United States); Lewis, J [University of California at Los Angeles, Los Angeles, CA (United States)

2016-06-15

Purpose: To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans. Results: Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm. Conclusion: The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application. This project was supported

SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients

International Nuclear Information System (INIS)

Dhou, S; Williams, C; Ionascu, D; Lewis, J

2016-01-01

Purpose: To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans. Results: Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm. Conclusion: The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application. This project was supported

Image based 3D city modeling : Comparative study

Directory of Open Access Journals (Sweden)

S. P. Singh

2014-06-01

Full Text Available 3D city model is a digital representation of the Earth’s surface and it’s related objects such as building, tree, vegetation, and some manmade feature belonging to urban area. The demand of 3D city modeling is increasing rapidly for various engineering and non-engineering applications. Generally four main image based approaches were used for virtual 3D city models generation. In first approach, researchers were used Sketch based modeling, second method is Procedural grammar based modeling, third approach is Close range photogrammetry based modeling and fourth approach is mainly based on Computer Vision techniques. SketchUp, CityEngine, Photomodeler and Agisoft Photoscan are the main softwares to represent these approaches respectively. These softwares have different approaches & methods suitable for image based 3D city modeling. Literature study shows that till date, there is no complete such type of comparative study available to create complete 3D city model by using images. This paper gives a comparative assessment of these four image based 3D modeling approaches. This comparative study is mainly based on data acquisition methods, data processing techniques and output 3D model products. For this research work, study area is the campus of civil engineering department, Indian Institute of Technology, Roorkee (India. This 3D campus acts as a prototype for city. This study also explains various governing parameters, factors and work experiences. This research work also gives a brief introduction, strengths and weakness of these four image based techniques. Some personal comment is also given as what can do or what can’t do from these softwares. At the last, this study shows; it concluded that, each and every software has some advantages and limitations. Choice of software depends on user requirements of 3D project. For normal visualization project, SketchUp software is a good option. For 3D documentation record, Photomodeler gives good

Ground Field-Based Hyperspectral Imaging: A Preliminary Study to Assess the Potential of Established Vegetation Indices to Infer Variation in Water-Use Efficiency.

Science.gov (United States)

Pelech, E. A.; McGrath, J.; Pederson, T.; Bernacchi, C.

2017-12-01

Increases in the global average temperature will consequently induce a higher occurrence of severe environmental conditions such as drought on arable land. To mitigate these threats, crops for fuel and food must be bred for higher water-use efficiencies (WUE). Defining genomic variation through high-throughput phenotypic analysis in field conditions has the potential to relieve the major bottleneck in linking desirable genetic traits to the associated phenotypic response. This can subsequently enable breeders to create new agricultural germplasm that supports the need for higher water-use efficient crops. From satellites to field-based aerial and ground sensors, the reflectance properties of vegetation measured by hyperspectral imaging is becoming a rapid high-throughput phenotyping technique. A variety of physiological traits can be inferred by regression analysis with leaf reflectance which is controlled by the properties and abundance of water, carbon, nitrogen and pigments. Although, given that the current established vegetation indices are designed to accentuate these properties from spectral reflectance, it becomes a challenge to infer relative measurements of WUE at a crop canopy scale without ground-truth data collection. This study aims to correlate established biomass and canopy-water-content indices with ground-truth data. Five bioenergy sorghum genotypes (Sorghum bicolor L. Moench) that have differences in WUE and wild-type Tobacco (Nicotiana tabacum var. Samsun) under irrigated and rainfed field conditions were examined. A linear regression analysis was conducted to determine if variation in canopy water content and biomass, driven by natural genotypic and artificial treatment influences, can be inferred using established vegetation indices. The results from this study will elucidate the ability of ground field-based hyperspectral imaging to assess variation in water content, biomass and water-use efficiency. This can lead to improved opportunities to

Steganalysis Techniques for Documents and Images

Science.gov (United States)

2005-05-01

steganography . We then illustrated the efficacy of our model using variations of LSB steganography . For binary images , we have made significant progress in...efforts have focused on two areas. The first area is LSB steganalysis for grayscale images . Here, as we had proposed (as a challenging task), we have...generalized our previous steganalysis technique of sample pair analysis to a theoretical framework for the detection of the LSB steganography . The new

The continuing challenge of understanding and modeling hemodynamic variation in fMRI

OpenAIRE

Handwerker, Daniel A.; Gonzalez-Castillo, Javier; D’Esposito, Mark; Bandettini, Peter A.

2012-01-01

Interpretation of fMRI data depends on our ability to understand or model the shape of the hemodynamic response (HR) to a neural event. Although the HR has been studied almost since the beginning of fMRI, we are still far from having robust methods to account for the full range of known HR variation in typical fMRI analyses. This paper reviews how the authors and others contributed to our understanding of HR variation. We present an overview of studies that describe HR variation across voxels...

The Halo Model of Origin Images

DEFF Research Database (Denmark)

Josiassen, Alexander; Lukas, Bryan A.; Whitwell, Gregory J.

2013-01-01

National origin has gained importance as a marketing tool for practitioners to sell their goods and services. However, because origin-image research has been troubled by several fundamental limitations, academia has become sceptical of the current status and strategic implications of the concept....... The aim of this paper was threefold, namely, to provide a state-of-the-art review of origin-image research in marketing, develop and empirically test a new origin-image model and, present the implications of the study....

The variational approach to the Glashow-Weinberg-Salam model

International Nuclear Information System (INIS)

Manka, R.; Sladkowski, J.

1987-01-01

The variational approach to the Glashow-Weinberg-Salam model, based on canonical quantization, is presented. It is shown that taking into consideration the Becchi-Rouet-Stora symmetry leads to the correct, temperature-dependent, effective potential. This generalization of the Weinberg-Coleman potential leads to a phase transition of the first kind

Image charge models for accurate construction of the electrostatic self-energy of 3D layered nanostructure devices

Science.gov (United States)

Barker, John R.; Martinez, Antonio

2018-04-01

Efficient analytical image charge models are derived for the full spatial variation of the electrostatic self-energy of electrons in semiconductor nanostructures that arises from dielectric mismatch using semi-classical analysis. The methodology provides a fast, compact and physically transparent computation for advanced device modeling. The underlying semi-classical model for the self-energy has been established and validated during recent years and depends on a slight modification of the macroscopic static dielectric constants for individual homogeneous dielectric regions. The model has been validated for point charges as close as one interatomic spacing to a sharp interface. A brief introduction to image charge methodology is followed by a discussion and demonstration of the traditional failure of the methodology to derive the electrostatic potential at arbitrary distances from a source charge. However, the self-energy involves the local limit of the difference between the electrostatic Green functions for the full dielectric heterostructure and the homogeneous equivalent. It is shown that high convergence may be achieved for the image charge method for this local limit. A simple re-normalisation technique is introduced to reduce the number of image terms to a minimum. A number of progressively complex 3D models are evaluated analytically and compared with high precision numerical computations. Accuracies of 1% are demonstrated. Introducing a simple technique for modeling the transition of the self-energy between disparate dielectric structures we generate an analytical model that describes the self-energy as a function of position within the source, drain and gated channel of a silicon wrap round gate field effect transistor on a scale of a few nanometers cross-section. At such scales the self-energies become large (typically up to ~100 meV) close to the interfaces as well as along the channel. The screening of a gated structure is shown to reduce the self

Image-Based Geometric Modeling and Mesh Generation

CERN Document Server

2013-01-01

As a new interdisciplinary research area, “image-based geometric modeling and mesh generation” integrates image processing, geometric modeling and mesh generation with finite element method (FEM) to solve problems in computational biomedicine, materials sciences and engineering. It is well known that FEM is currently well-developed and efficient, but mesh generation for complex geometries (e.g., the human body) still takes about 80% of the total analysis time and is the major obstacle to reduce the total computation time. It is mainly because none of the traditional approaches is sufficient to effectively construct finite element meshes for arbitrarily complicated domains, and generally a great deal of manual interaction is involved in mesh generation. This contributed volume, the first for such an interdisciplinary topic, collects the latest research by experts in this area. These papers cover a broad range of topics, including medical imaging, image alignment and segmentation, image-to-mesh conversion,...

Variations of little Higgs models and their electroweak constraints

International Nuclear Information System (INIS)

Csaki, Csaba; Hubisz, Jay; Meade, Patrick; Kribs, Graham D.; Terning, John

2003-01-01

We calculate the tree-level electroweak precision constraints on a wide class of little Higgs models including variations of the littlest Higgs SU(5)/SO(5), SU(6)/Sp(6), and SU(4) 4 /SU(3) 4 models. By performing a global fit to the precision data we find that for generic regions of the parameter space the bound on the symmetry breaking scale f is several TeV, where we have kept the normalization of f constant in the different models. For example, the 'minimal' implementation of SU(6)/Sp(6) is bounded by f>3.0 TeV throughout most of the parameter space, and SU(4) 4 /SU(3) 4 is bounded by f 2 ≡f 1 2 +f 2 2 >(4.2 TeV) 2 . In certain models, such as SU(4) 4 /SU(3) 4 , a large f does not directly imply a large amount of fine-tuning since the heavy-fermion masses that contribute to the Higgs boson mass can be lowered below f for a carefully chosen set of parameters. We also find that for certain models (or variations) there exist regions of parameter space in which the bound on f can be lowered into the range 1-2 TeV. These regions are typically characterized by a small mixing between heavy and standard model gauge bosons and a small (or vanishing) coupling between heavy U(1) gauge bosons and light fermions. Whether such a region of parameter space is natural or not is ultimately contingent on the UV completion

Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

Science.gov (United States)

Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

2015-12-01

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

Variational cluster perturbation theory for Bose-Hubbard models

International Nuclear Information System (INIS)

Koller, W; Dupuis, N

2006-01-01

We discuss the application of the variational cluster perturbation theory (VCPT) to the Mott-insulator-to-superfluid transition in the Bose-Hubbard model. We show how the VCPT can be formulated in such a way that it gives a translation invariant excitation spectrum-free of spurious gaps-despite the fact that it formally breaks translation invariance. The phase diagram and the single-particle Green function in the insulating phase are obtained for one-dimensional systems. When the chemical potential of the cluster is taken as a variational parameter, the VCPT reproduces the dimensional dependence of the phase diagram even for one-site clusters. We find a good quantitative agreement with the results of the density-matrix renormalization group when the number of sites in the cluster becomes of order 10. The extension of the method to the superfluid phase is discussed

Fuzzy object models for newborn brain MR image segmentation

Science.gov (United States)

Kobashi, Syoji; Udupa, Jayaram K.

2013-03-01

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

Seasonal Gravity Field Variations from GRACE and Hydrological Models

DEFF Research Database (Denmark)

Andersen, Ole Baltazar; Hinderer, Jacques; Lemoine, Frank G.

2004-01-01

. Four global hydrological models covering the same period in 2002–2003 as the GRACE observations were investigated to for their mutual consistency in estimates of annual variation in terrestrial water storage and related temporal changes in gravity field. The hydrological models differ by a maximum of 2...... µGal or nearly 5 cm equivalent water storage in selected regions. Integrated over all land masses the standard deviation among the annual signal from the four hydrological models are 0.6 µGal equivalent to around 1.4 cm in equivalent water layer thickness. The estimated accuracy of the annual...

Iterative choice of the optimal regularization parameter in TV image deconvolution

International Nuclear Information System (INIS)

Sixou, B; Toma, A; Peyrin, F; Denis, L

2013-01-01

We present an iterative method for choosing the optimal regularization parameter for the linear inverse problem of Total Variation image deconvolution. This approach is based on the Morozov discrepancy principle and on an exponential model function for the data term. The Total Variation image deconvolution is performed with the Alternating Direction Method of Multipliers (ADMM). With a smoothed l 2 norm, the differentiability of the value of the Lagrangian at the saddle point can be shown and an approximate model function obtained. The choice of the optimal parameter can be refined with a Newton method. The efficiency of the method is demonstrated on a blurred and noisy bone CT cross section

Statistical metrology - measurement and modeling of variation for advanced process development and design rule generation

International Nuclear Information System (INIS)

Boning, Duane S.; Chung, James E.

1998-01-01

Advanced process technology will require more detailed understanding and tighter control of variation in devices and interconnects. The purpose of statistical metrology is to provide methods to measure and characterize variation, to model systematic and random components of that variation, and to understand the impact of variation on both yield and performance of advanced circuits. Of particular concern are spatial or pattern-dependencies within individual chips; such systematic variation within the chip can have a much larger impact on performance than wafer-level random variation. Statistical metrology methods will play an important role in the creation of design rules for advanced technologies. For example, a key issue in multilayer interconnect is the uniformity of interlevel dielectric (ILD) thickness within the chip. For the case of ILD thickness, we describe phases of statistical metrology development and application to understanding and modeling thickness variation arising from chemical-mechanical polishing (CMP). These phases include screening experiments including design of test structures and test masks to gather electrical or optical data, techniques for statistical decomposition and analysis of the data, and approaches to calibrating empirical and physical variation models. These models can be integrated with circuit CAD tools to evaluate different process integration or design rule strategies. One focus for the generation of interconnect design rules are guidelines for the use of 'dummy fill' or 'metal fill' to improve the uniformity of underlying metal density and thus improve the uniformity of oxide thickness within the die. Trade-offs that can be evaluated via statistical metrology include the improvements to uniformity possible versus the effect of increased capacitance due to additional metal

Fuzzy modeling of electrical impedance tomography images of the lungs

International Nuclear Information System (INIS)

Tanaka, Harki; Ortega, Neli Regina Siqueira; Galizia, Mauricio Stanzione; Borges, Joao Batista; Amato, Marcelo Britto Passos

2008-01-01

Objectives: Aiming to improve the anatomical resolution of electrical impedance tomography images, we developed a fuzzy model based on electrical impedance tomography's high temporal resolution and on the functional pulmonary signals of perfusion and ventilation. Introduction: Electrical impedance tomography images carry information about both ventilation and perfusion. However, these images are difficult to interpret because of insufficient anatomical resolution, such that it becomes almost impossible to distinguish the heart from the lungs. Methods: Electrical impedance tomography data from an experimental animal model were collected during normal ventilation and apnoea while an injection of hypertonic saline was administered. The fuzzy model was elaborated in three parts: a modeling of the heart, the pulmonary ventilation map and the pulmonary perfusion map. Image segmentation was performed using a threshold method, and a ventilation/perfusion map was generated. Results: Electrical impedance tomography images treated by the fuzzy model were compared with the hypertonic saline injection method and computed tomography scan images, presenting good results. The average accuracy index was 0.80 when comparing the fuzzy modeled lung maps and the computed tomography scan lung mask. The average ROC curve area comparing a saline injection image and a fuzzy modeled pulmonary perfusion image was 0.77. Discussion: The innovative aspects of our work are the use of temporal information for the delineation of the heart structure and the use of two pulmonary functions for lung structure delineation. However, robustness of the method should be tested for the imaging of abnormal lung conditions. Conclusions: These results showed the adequacy of the fuzzy approach in treating the anatomical resolution uncertainties in electrical impedance tomography images. (author)

Mapping using the Tsyganenko long magnetospheric model and its relationship to Viking auroral images

International Nuclear Information System (INIS)

Elphinstone, R.D.; Hearn, D.; Murphree, J.S.; Cogger, L.L.

1991-01-01

The Tsyganenko long magnetospheric model (1987) has been used in conjunction with ultra-violet images taken by the Viking spacecraft to investigate the relationship of the auroral distribution to different magnetospheric regions. The model describes the large-scale structure of the magnetosphere reasonably well for dipole tilt angles near zero, but it appears to break down at higher tilt angles. Even so, a wide variety of auroral configurations can be accurately described by the model. It appears that the open-closed field line boundary is a poor indicator of auroral arc systems with the possible exception of high-latitude polar arcs. The auroral distribution typically called the oval maps to a region in the equatorial plane quite close to the Earth and can be approximately located by mapping the model current density maximum from the equatorial plane into the ionosphere. Although the model may break down along the flanks of the magnetotail, the large-scale auroral distribution generally reflects variations in the near-Earth region and can be modeled quite effectively

Modeling of Thermospheric Neutral Density Variations in Response to Geomagnetic Forcing using GRACE Accelerometer Data

Science.gov (United States)

Calabia, A.; Matsuo, T.; Jin, S.

2017-12-01

The upper atmospheric expansion refers to an increase in the temperature and density of Earth's thermosphere due to increased geomagnetic and space weather activities, producing anomalous atmospheric drag on LEO spacecraft. Increased drag decelerates satellites, moving their orbit closer to Earth, decreasing the lifespan of satellites, and making satellite orbit determination difficult. In this study, thermospheric neutral density variations due to geomagnetic forcing are investigated from 10 years (2003-2013) of GRACE's accelerometer-based estimates. In order to isolate the variations produced by geomagnetic forcing, 99.8% of the total variability has been modeled and removed through the parameterization of annual, LST, and solar-flux variations included in the primary Empirical Orthogonal Functions. The residual disturbances of neutral density variations have been investigated further in order to unravel their relationship to several geomagnetic indices and space weather activity indicators. Stronger fluctuations have been found in the southern polar cap, following the dipole-tilt angle variations. While the parameterization of the residual disturbances in terms of Dst index results in the best fit to training data, the use of merging electric field as a predictor leads to the best forecasting performance. An important finding is that modeling of neutral density variations in response geomagnetic forcing can be improved by accounting for the latitude-dependent delay. Our data-driven modeling results are further compared to modeling with TIEGCM.

A probabilistic cell model in background corrected image sequences for single cell analysis

Directory of Open Access Journals (Sweden)

Fieguth Paul

2010-10-01

Full Text Available Abstract Background Methods of manual cell localization and outlining are so onerous that automated tracking methods would seem mandatory for handling huge image sequences, nevertheless manual tracking is, astonishingly, still widely practiced in areas such as cell biology which are outside the influence of most image processing research. The goal of our research is to address this gap by developing automated methods of cell tracking, localization, and segmentation. Since even an optimal frame-to-frame association method cannot compensate and recover from poor detection, it is clear that the quality of cell tracking depends on the quality of cell detection within each frame. Methods Cell detection performs poorly where the background is not uniform and includes temporal illumination variations, spatial non-uniformities, and stationary objects such as well boundaries (which confine the cells under study. To improve cell detection, the signal to noise ratio of the input image can be increased via accurate background estimation. In this paper we investigate background estimation, for the purpose of cell detection. We propose a cell model and a method for background estimation, driven by the proposed cell model, such that well structure can be identified, and explicitly rejected, when estimating the background. Results The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably. The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC image sequences are quite promising. Conclusion The understanding of cell behavior relies on precise information about the temporal dynamics and spatial distribution of cells. Such information may play a key role in disease research and regenerative medicine, so automated methods for observation and measurement of cells from microscopic images are in high demand. The proposed method in this paper is capable

A Variational Model for Two-Phase Immiscible Electroosmotic Flow at Solid Surfaces

KAUST Repository

Shao, Sihong; Qian, Tiezheng

2012-01-01

We develop a continuum hydrodynamic model for two-phase immiscible flows that involve electroosmotic effect in an electrolyte and moving contact line at solid surfaces. The model is derived through a variational approach based on the Onsager

A statistical model for radar images of agricultural scenes

Science.gov (United States)

Frost, V. S.; Shanmugan, K. S.; Holtzman, J. C.; Stiles, J. A.

1982-01-01

The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.

Fisheye image rectification using spherical and digital distortion models

Science.gov (United States)

Li, Xin; Pi, Yingdong; Jia, Yanling; Yang, Yuhui; Chen, Zhiyong; Hou, Wenguang

2018-02-01

Fisheye cameras have been widely used in many applications including close range visual navigation and observation and cyber city reconstruction because its field of view is much larger than that of a common pinhole camera. This means that a fisheye camera can capture more information than a pinhole camera in the same scenario. However, the fisheye image contains serious distortion, which may cause trouble for human observers in recognizing the objects within. Therefore, in most practical applications, the fisheye image should be rectified to a pinhole perspective projection image to conform to human cognitive habits. The traditional mathematical model-based methods cannot effectively remove the distortion, but the digital distortion model can reduce the image resolution to some extent. Considering these defects, this paper proposes a new method that combines the physical spherical model and the digital distortion model. The distortion of fisheye images can be effectively removed according to the proposed approach. Many experiments validate its feasibility and effectiveness.

Image based Monte Carlo modeling for computational phantom

International Nuclear Information System (INIS)

Cheng, M.; Wang, W.; Zhao, K.; Fan, Y.; Long, P.; Wu, Y.

2013-01-01

Full text of the publication follows. The evaluation on the effects of ionizing radiation and the risk of radiation exposure on human body has been becoming one of the most important issues for radiation protection and radiotherapy fields, which is helpful to avoid unnecessary radiation and decrease harm to human body. In order to accurately evaluate the dose on human body, it is necessary to construct more realistic computational phantom. However, manual description and verification of the models for Monte Carlo (MC) simulation are very tedious, error-prone and time-consuming. In addition, it is difficult to locate and fix the geometry error, and difficult to describe material information and assign it to cells. MCAM (CAD/Image-based Automatic Modeling Program for Neutronics and Radiation Transport Simulation) was developed as an interface program to achieve both CAD- and image-based automatic modeling. The advanced version (Version 6) of MCAM can achieve automatic conversion from CT/segmented sectioned images to computational phantoms such as MCNP models. Imaged-based automatic modeling program(MCAM6.0) has been tested by several medical images and sectioned images. And it has been applied in the construction of Rad-HUMAN. Following manual segmentation and 3D reconstruction, a whole-body computational phantom of Chinese adult female called Rad-HUMAN was created by using MCAM6.0 from sectioned images of a Chinese visible human dataset. Rad-HUMAN contains 46 organs/tissues, which faithfully represented the average anatomical characteristics of the Chinese female. The dose conversion coefficients (Dt/Ka) from kerma free-in-air to absorbed dose of Rad-HUMAN were calculated. Rad-HUMAN can be applied to predict and evaluate dose distributions in the Treatment Plan System (TPS), as well as radiation exposure for human body in radiation protection. (authors)

Sparse representation based image interpolation with nonlocal autoregressive modeling.

Science.gov (United States)

Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming

2013-04-01

Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.

TOTAL WOOD VOLUME ESTIMATION OF EUCALYPTUS SPECIES BY IMAGES OF LANDSAT SATELLITE

Directory of Open Access Journals (Sweden)

Elias Fernando Berra

2012-12-01

Full Text Available http://dx.doi.org/10.5902/198050987566Models relating spectral answers with biophysical parameters aim estimate variables, like wood volume, without the necessity of frequent field measurements. The objective was to develop models to estimate wood volume by Landsat 5 TM images, supported by regional forest inventory data. The image was geo-referenced and converted to spectral reflectance. After, the images-index NDVI (Normalized Difference Vegetation Index and SR (Simple Ratio was generated. The reflectance values of the bands (TM1, TM2, TM3 e TM4 and of the indices (NDVI and SR was related with the wood volume. The biggest correlation with volume was with the NDVI and SR indices. The variables selection was made by Stepwise method, which returned three regression models as significant to explain the variation in volume. Finally, the best fitted model was selected (volume = -830,95 + 46,05 (SR + 107,47 (TM2, which was applied on the Landsat image where the pixels had started to represent the estimated volume in m³/ha on the Eucalyptus sp. production units. This model, significant at 95% confidence level, explains 68% of the wood volume variation.

Mathematical models for correction of images, obtained at radioisotope scan

International Nuclear Information System (INIS)

Glaz, A.; Lubans, A.

2002-01-01

The images, which obtained at radioisotope scintigraphy, contain distortions. Distortions appear as a result of absorption of radiation by patient's body's tissues. Two mathematical models for reducing of such distortions are proposed. Image obtained by only one gamma camera is used in the first mathematical model. Unfortunately, this model allows processing of the images only in case, when it can be assumed, that the investigated organ has a symmetric form. The images obtained by two gamma cameras are used in the second model. It gives possibility to assume that the investigated organ has non-symmetric form and to acquire more precise results. (authors)

A 4DCT imaging-based breathing lung model with relative hysteresis

Energy Technology Data Exchange (ETDEWEB)

Miyawaki, Shinjiro; Choi, Sanghun [IIHR – Hydroscience & Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Hoffman, Eric A. [Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Medicine, The University of Iowa, Iowa City, IA 52242 (United States); Department of Radiology, The University of Iowa, Iowa City, IA 52242 (United States); Lin, Ching-Long, E-mail: ching-long-lin@uiowa.edu [IIHR – Hydroscience & Engineering, The University of Iowa, Iowa City, IA 52242 (United States); Department of Mechanical and Industrial Engineering, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242 (United States)

2016-12-01

To reproduce realistic airway motion and airflow, the authors developed a deforming lung computational fluid dynamics (CFD) model based on four-dimensional (4D, space and time) dynamic computed tomography (CT) images. A total of 13 time points within controlled tidal volume respiration were used to account for realistic and irregular lung motion in human volunteers. Because of the irregular motion of 4DCT-based airways, we identified an optimal interpolation method for airway surface deformation during respiration, and implemented a computational solid mechanics-based moving mesh algorithm to produce smooth deforming airway mesh. In addition, we developed physiologically realistic airflow boundary conditions for both models based on multiple images and a single image. Furthermore, we examined simplified models based on one or two dynamic or static images. By comparing these simplified models with the model based on 13 dynamic images, we investigated the effects of relative hysteresis of lung structure with respect to lung volume, lung deformation, and imaging methods, i.e., dynamic vs. static scans, on CFD-predicted pressure drop. The effect of imaging method on pressure drop was 24 percentage points due to the differences in airflow distribution and airway geometry. - Highlights: • We developed a breathing human lung CFD model based on 4D-dynamic CT images. • The 4DCT-based breathing lung model is able to capture lung relative hysteresis. • A new boundary condition for lung model based on one static CT image was proposed. • The difference between lung models based on 4D and static CT images was quantified.

Modelling basin-wide variations in Amazon forest photosynthesis

Science.gov (United States)

Mercado, Lina; Lloyd, Jon; Domingues, Tomas; Fyllas, Nikolaos; Patino, Sandra; Dolman, Han; Sitch, Stephen

2010-05-01

Given the importance of Amazon rainforest in the global carbon and hydrological cycles, there is a need to use parameterized and validated ecosystem gas exchange and vegetation models for this region in order to adequately simulate present and future carbon and water balances. Recent research has found major differences in above-ground net primary productivity (ANPP), above ground biomass and tree dynamics across Amazonia. West Amazonia is more dynamic, with younger trees, higher stem growth rates and lower biomass than central and eastern Amazon (Baker et al. 2004; Malhi et al. 2004; Phillips et al. 2004). A factor of three variation in above-ground net primary productivity has been estimated across Amazonia by Malhi et al. (2004). Different hypotheses have been proposed to explain the observed spatial variability in ANPP (Malhi et al. 2004). First, due to the proximity to the Andes, sites from western Amazonia tend to have richer soils than central and eastern Amazon and therefore soil fertility could possibly be highly related to the high wood productivity found in western sites. Second, if GPP does not vary across the Amazon basin then different patterns of carbon allocation to respiration could also explain the observed ANPP gradient. However since plant growth depends on the interaction between photosynthesis, transport of assimilates, plant respiration, water relations and mineral nutrition, variations in plant gross photosynthesis (GPP) could also explain the observed variations in ANPP. In this study we investigate whether Amazon GPP can explain variations of observed ANPP. We use a sun and shade canopy gas exchange model that has been calibrated and evaluated at five rainforest sites (Mercado et al. 2009) to simulate gross primary productivity of 50 sites across the Amazon basin during the period 1980-2001. Such simulation differs from the ones performed with global vegetation models (Cox et al. 1998; Sitch et al. 2003) where i) single plant functional

From medical imaging data to 3D printed anatomical models.

Directory of Open Access Journals (Sweden)

Thore M Bücking

Full Text Available Anatomical models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D printers have made it possible to create cost-efficient patient-specific models without expert knowledge. We introduce a general workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT to 3D printed physical models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an anatomical model from medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.

Finite Element Calculation of Local Variation in the Driving Force for Austenite to Martensite Transformation

International Nuclear Information System (INIS)

Datta, K.; Geijselaers, H. J. M.; Huetink, J.; Post, J.; Dinsdale, A.

2007-01-01

The mechanics and thermodynamics of strain induced martensitic transformation are coupled for a metastable alloy steel and implemented in FE models of forming processes. The basic formulations are based on a fifty year old treaty by Patel and Cohen. The variation in Gibbs energy due to local variation in strain, strain rate, temperature and state of stress of a forming part is calculated by FE codes. The local variation in Gibbs energy gives a probabilistic image of the potential sites for strain induced martensitic transformations

Menu variations for diabetes mellitus patients using Goal Programming model

Science.gov (United States)

Dhoruri, Atmini; Lestari, Dwi; Ratnasari, Eminugroho

2017-08-01

Diabetes mellitus (DM) was a chronic metabolic disease characterized by higher than normal blood glucose level (normal blood glucose level = = 80 -120 mg/dl). In this study, type 2 DM which mostly caused by unhealthy eating habits would be investigated. Related to eating habit, DM patients needed dietary menu planning with an extracare regarding their nutrients intake (energy, protein, fat and carbohydrate). Therefore, the measures taken were by organizing nutritious dietary menu for diabetes mellitus patients. Dietary menu with appropriate amount of nutrients was organized by considering the amount of calories, proteins, fats and carbohydrates. In this study, Goal Programming model was employed to determine optimal dietary menu variations for diabetes mellitus patients by paying attention to optimal expenses. According to the data obtained from hospitals in Yogyakarta, optimal menu variations would be analyzed by using Goal Programming model and would be completed by using LINGO computer program.

Solid models for CT/MR image display

International Nuclear Information System (INIS)

ManKovich, N.J.; Yue, A.; Kioumehr, F.; Ammirati, M.; Turner, S.

1991-01-01

Medical imaging can now take wider advantage of Computer-Aided-Manufacturing through rapid prototyping technologies (RPT) such as stereolithography, laser sintering, and laminated object manufacturing to directly produce solid models of patient anatomy from processed CT and MR images. While conventional surgical planning relies on consultation with the radiologist combined with direct reading and measurement of CT and MR studies, 3-D surface and volumetric display workstations are providing a more easily interpretable view of patient anatomy. RPT can provide the surgeon with a life size model of patient anatomy constructed layer by layer with full internal detail. The authors have developed a prototype image processing and model fabrication system based on stereolithography, which provides the neurosurgeon with models of the skull base. Parallel comparison of the mode with the original thresholded CT data and with a CRT displayed surface rendering showed that both have an accuracy of >99.6 percent. The measurements on the surface rendered display proved more difficult to exactly locate and yielded a standard deviation of 2.37 percent. This paper presents an accuracy study and discusses ways of assessing the quality of neurosurgical plans when 3-D models re made available as planning tools

Modeling digital breast tomosynthesis imaging systems for optimization studies

Science.gov (United States)

Lau, Beverly Amy

Digital breast tomosynthesis (DBT) is a new imaging modality for breast imaging. In tomosynthesis, multiple images of the compressed breast are acquired at different angles, and the projection view images are reconstructed to yield images of slices through the breast. One of the main problems to be addressed in the development of DBT is the optimal parameter settings to obtain images ideal for detection of cancer. Since it would be unethical to irradiate women multiple times to explore potentially optimum geometries for tomosynthesis, it is ideal to use a computer simulation to generate projection images. Existing tomosynthesis models have modeled scatter and detector without accounting for oblique angles of incidence that tomosynthesis introduces. Moreover, these models frequently use geometry-specific physical factors measured from real systems, which severely limits the robustness of their algorithms for optimization. The goal of this dissertation was to design the framework for a computer simulation of tomosynthesis that would produce images that are sensitive to changes in acquisition parameters, so an optimization study would be feasible. A computer physics simulation of the tomosynthesis system was developed. The x-ray source was modeled as a polychromatic spectrum based on published spectral data, and inverse-square law was applied. Scatter was applied using a convolution method with angle-dependent scatter point spread functions (sPSFs), followed by scaling using an angle-dependent scatter-to-primary ratio (SPR). Monte Carlo simulations were used to generate sPSFs for a 5-cm breast with a 1-cm air gap. Detector effects were included through geometric propagation of the image onto layers of the detector, which were blurred using depth-dependent detector point-spread functions (PRFs). Depth-dependent PRFs were calculated every 5-microns through a 200-micron thick CsI detector using Monte Carlo simulations. Electronic noise was added as Gaussian noise as a

Mangrove forests submitted to depositional processes and salinity variation investigated using satellite images and vegetation structure surveys

OpenAIRE

Cunha-Lignon, M.; Kampel, M.; Menghini, R.P.; Schaeffer-Novelli, Y.; Cintrón, G.; Dahdouh-Guebas, F.

2011-01-01

The current paper examines the growth and spatio-temporal variation of mangrove forests in response to depositional processes and different salinity conditions. Data from mangrove vegetation structure collected at permanent plots and satellite images were used. In the northern sector important environmental changes occurred due to an artificial channel producing modifications in salinity. The southern sector is considered the best conserved mangrove area along the coast of São Paulo State, Br...

30th International School of Mathematics "G Stampacchia" : Equilibrium Problems and Variational Models "Ettore Majorana"

CERN Document Server

Giannessi, Franco; Maugeri, Antonino; Equilibrium Problems and Variational Models

2000-01-01

The volume, devoted to variational analysis and its applications, collects selected and refereed contributions, which provide an outline of the field. The meeting of the title "Equilibrium Problems and Variational Models", which was held in Erice (Sicily) in the period June 23 - July 2 2000, was the occasion of the presentation of some of these papers; other results are a consequence of a fruitful and constructive atmosphere created during the meeting. New results, which enlarge the field of application of variational analysis, are presented in the book; they deal with the vectorial analysis, time dependent variational analysis, exact penalization, high order deriva­ tives, geometric aspects, distance functions and log-quadratic proximal methodology. The new theoretical results allow one to improve in a remarkable way the study of significant problems arising from the applied sciences, as continuum model of transportation, unilateral problems, multicriteria spatial price models, network equilibrium...

SU-E-I-53: Variation in Measurements of Breast Skin Thickness Obtained Using Different Imaging Modalities

International Nuclear Information System (INIS)

Nguyen, U; Kumaraswamy, N; Markey, M

2014-01-01

Purpose: To investigate variation in measurements of breast skin thickness obtained using different imaging modalities, including mammography, computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI). Methods: Breast skin thicknesses as measured by mammography, CT, ultrasound, and MRI were compared. Mammographic measurements of skin thickness were obtained from published studies that utilized standard positioning (upright) and compression. CT measurements of skin thickness were obtained from a published study of a prototype breast CT scanner in which the women were in the prone position and the breast was uncompressed. Dermatological ultrasound exams of the breast skin were conducted at our institution, with the subjects in the upright position and the breast uncompressed. Breast skin thickness was calculated from breast MRI exams at our institution, with the patient in the prone position and the breast uncompressed. Results: T tests for independent samples demonstrated significant differences in the mean breast skin thickness as measured by different imaging modalities. Repeated measures ANOVA revealed significant differences in breast skin thickness across different quadrants of the breast for some modalities. Conclusion: The measurement of breast skin thickness is significantly different across different imaging modalities. Differences in the amount of compression and differences in patient positioning are possible reasons why measurements of breast skin thickness vary by modality

Development and tests of a mouse voxel model dor MCNPX based on Digimouse images

Energy Technology Data Exchange (ETDEWEB)

Melo M, B.; Ferreira F, C. [Centro de Desenvolvimento da Tecnologia Nuclear / CNEN, Pte. Antonio Carlos No. 6627, Belo Horizonte 31270-901, Minas Gerais (Brazil); Garcia de A, I.; Machado T, B.; Passos Ribeiro de C, T., E-mail: bmm@cdtn.br [Universidade Federal de Minas Gerais, Departamento de Engenharia Nuclear, Pte. Antonio Carlos 6627, Belo Horizonte 31270-901, Minas Gerais (Brazil)

2015-10-15

Mice have been widely used in experimental protocols involving ionizing radiation. Biological effects (Be) induced by radiation can compromise studies results. Good estimates of mouse whole body and organs absorbed dose could provide valuable information to researchers. The aim of this study was to create and test a new voxel phantom for mice dosimetry from -Digimouse- project images. Micro CT images from Digimouse project were used in this work. Corel PHOTOPAINT software was utilized in segmentation process. The three-dimensional (3-D) model assembly and its voxel size manipulation were performed by Image J. SISCODES was used to adapt the model to run in MCNPX Monte Carlo code. The resulting model was called DM{sub B}RA. The volume and mass of segmented organs were compared with data available in literature. For the preliminary tests the heart was considered the source organ. Photons of diverse energies were simulated and Saf values obtained through F6:p and + F6 MCNPX tallies. The results were compared with reference data. 3-D picturing of absorbed doses patterns and relative errors distribution were generated by a C++ -in house- made program and visualized through Amide software. The organ masses of DM{sub B}RA correlated well with two models that were based on same set of images. However some organs, like eyes and adrenals, skeleton and brain showed large discrepancies. Segmentation of an identical image set by different persons and/or methods can result significant organ masses variations. We believe that the main causes of these differences were: i) operator dependent subjectivity in the definition of organ limits during the segmentation processes; and i i) distinct voxel dimensions between evaluated models. Lack of reference data for mice models construction and dosimetry was detected. Comparison with other models originated from different mice strains also demonstrated that the anatomical and size variability can be significant. Use of + F6 tally for mouse

Development and tests of a mouse voxel model dor MCNPX based on Digimouse images

International Nuclear Information System (INIS)

Melo M, B.; Ferreira F, C.; Garcia de A, I.; Machado T, B.; Passos Ribeiro de C, T.

2015-10-01

Mice have been widely used in experimental protocols involving ionizing radiation. Biological effects (Be) induced by radiation can compromise studies results. Good estimates of mouse whole body and organs absorbed dose could provide valuable information to researchers. The aim of this study was to create and test a new voxel phantom for mice dosimetry from -Digimouse- project images. Micro CT images from Digimouse project were used in this work. Corel PHOTOPAINT software was utilized in segmentation process. The three-dimensional (3-D) model assembly and its voxel size manipulation were performed by Image J. SISCODES was used to adapt the model to run in MCNPX Monte Carlo code. The resulting model was called DM B RA. The volume and mass of segmented organs were compared with data available in literature. For the preliminary tests the heart was considered the source organ. Photons of diverse energies were simulated and Saf values obtained through F6:p and + F6 MCNPX tallies. The results were compared with reference data. 3-D picturing of absorbed doses patterns and relative errors distribution were generated by a C++ -in house- made program and visualized through Amide software. The organ masses of DM B RA correlated well with two models that were based on same set of images. However some organs, like eyes and adrenals, skeleton and brain showed large discrepancies. Segmentation of an identical image set by different persons and/or methods can result significant organ masses variations. We believe that the main causes of these differences were: i) operator dependent subjectivity in the definition of organ limits during the segmentation processes; and i i) distinct voxel dimensions between evaluated models. Lack of reference data for mice models construction and dosimetry was detected. Comparison with other models originated from different mice strains also demonstrated that the anatomical and size variability can be significant. Use of + F6 tally for mouse phantoms

Monte Carlo modeling of human tooth optical coherence tomography imaging

International Nuclear Information System (INIS)

Shi, Boya; Meng, Zhuo; Wang, Longzhi; Liu, Tiegen

2013-01-01

We present a Monte Carlo model for optical coherence tomography (OCT) imaging of human tooth. The model is implemented by combining the simulation of a Gaussian beam with simulation for photon propagation in a two-layer human tooth model with non-parallel surfaces through a Monte Carlo method. The geometry and the optical parameters of the human tooth model are chosen on the basis of the experimental OCT images. The results show that the simulated OCT images are qualitatively consistent with the experimental ones. Using the model, we demonstrate the following: firstly, two types of photons contribute to the information of morphological features and noise in the OCT image of a human tooth, respectively. Secondly, the critical imaging depth of the tooth model is obtained, and it is found to decrease significantly with increasing mineral loss, simulated as different enamel scattering coefficients. Finally, the best focus position is located below and close to the dental surface by analysis of the effect of focus positions on the OCT signal and critical imaging depth. We anticipate that this modeling will become a powerful and accurate tool for a preliminary numerical study of the OCT technique on diseases of dental hard tissue in human teeth. (paper)

Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise.

Science.gov (United States)

Zhang, Jiachao; Hirakawa, Keigo

2017-04-01

This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. A quantile analysis in pixel, wavelet transform, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed to correct the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we propose a mixture of Poisson denoising method to remove the denoising artifacts without affecting image details, such as edge and textures. Experiments with real sensor data verify that denoising for real image sensor data is indeed improved by this new technique.

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

Science.gov (United States)

Papandreou, George; Chen, Liang-Chieh; Yuille, Alan L.

2015-01-01

The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient of the proposed model is a compact dictionary of mini-epitomes, learned in an unsupervised fashion on a large collection of images. The use of epitomes allows us to explicitly account for photometric and position variability in image appearance. We show that this flexibility considerably increases the capacity of the dictionary to accurately approximate the appearance of image patches and support recognition tasks. For image classification, we develop histogram-based image encoding methods tailored to the epitomic representation, as well as an “epitomic footprint” encoding which is easy to visualize and highlights the generative nature of our model. We discuss in detail computational aspects and develop efficient algorithms to make the model scalable to large tasks. The proposed techniques are evaluated with experiments on the challenging PASCAL VOC 2007 image classification benchmark. PMID:26321859

The cumulative verification image analysis tool for offline evaluation of portal images

International Nuclear Information System (INIS)

Wong, John; Yan Di; Michalski, Jeff; Graham, Mary; Halverson, Karen; Harms, William; Purdy, James

1995-01-01

Purpose: Daily portal images acquired using electronic portal imaging devices contain important information about the setup variation of the individual patient. The data can be used to evaluate the treatment and to derive correction for the individual patient. The large volume of images also require software tools for efficient analysis. This article describes the approach of cumulative verification image analysis (CVIA) specifically designed as an offline tool to extract quantitative information from daily portal images. Methods and Materials: The user interface, image and graphics display, and algorithms of the CVIA tool have been implemented in ANSCI C using the X Window graphics standards. The tool consists of three major components: (a) definition of treatment geometry and anatomical information; (b) registration of portal images with a reference image to determine setup variation; and (c) quantitative analysis of all setup variation measurements. The CVIA tool is not automated. User interaction is required and preferred. Successful alignment of anatomies on portal images at present remains mostly dependent on clinical judgment. Predefined templates of block shapes and anatomies are used for image registration to enhance efficiency, taking advantage of the fact that much of the tool's operation is repeated in the analysis of daily portal images. Results: The CVIA tool is portable and has been implemented on workstations with different operating systems. Analysis of 20 sequential daily portal images can be completed in less than 1 h. The temporal information is used to characterize setup variation in terms of its systematic, random and time-dependent components. The cumulative information is used to derive block overlap isofrequency distributions (BOIDs), which quantify the effective coverage of the prescribed treatment area throughout the course of treatment. Finally, a set of software utilities is available to facilitate feedback of the information for

Imaging of salt structure; Gan`enso kozo no imaging

Energy Technology Data Exchange (ETDEWEB)

Akama, K; Saeki, T [Japan National Corp., Tokyo (Japan). Technology Research Center; Matsuoka, T [Japan Petroleum Exploration Corp., Tokyo (Japan)

1996-10-01

Due to the improvement of algorithm and the advancement of calculation performance, the imaging by depth migration before stacking is being put into practice from the viewpoint of both calculation cost and accuracy. A lot of imaging examples have been already reported from the survey areas with complicated velocity structures, such as the North Sea and the Gulf of Mexico. Effectiveness of the method has been confirmed. For imaging techniques in Japan National Oil Corporation and Japan Petroleum Exploration Co., Ltd., high-speed depth migration before stacking and high efficiency velocity structure estimation technique have been investigated. This paper describes necessary care to be taken when using depth focusing analysis (DFA) for correcting a velocity model, as an interim stage of case study. The results of depth migration before stacking using dip moveout (DMO) velocity were further inferior to the section obtained by the migration after tracking. Tendency of velocity errors was distinctly deviated with the variation of depth and horizontal position. It was not suitable for modifying the velocity model using DFA. 4 refs., 4 figs.

Nanoscale elastic modulus variation in loaded polymeric micelle reactors.

Science.gov (United States)

Solmaz, Alim; Aytun, Taner; Deuschle, Julia K; Ow-Yang, Cleva W

2012-07-17

Tapping mode atomic force microscopy (TM-AFM) enables mapping of chemical composition at the nanoscale by taking advantage of the variation in phase angle shift arising from an embedded second phase. We demonstrate that phase contrast can be attributed to the variation in elastic modulus during the imaging of zinc acetate (ZnAc)-loaded reverse polystyrene-block-poly(2-vinylpyridine) (PS-b-P2VP) diblock co-polymer micelles less than 100 nm in diameter. Three sample configurations were characterized: (i) a 31.6 μm thick polystyrene (PS) support film for eliminating the substrate contribution, (ii) an unfilled PS-b-P2VP micelle supported by the same PS film, and (iii) a ZnAc-loaded PS-b-P2VP micelle supported by the same PS film. Force-indentation (F-I) curves were measured over unloaded micelles on the PS film and over loaded micelles on the PS film, using standard tapping mode probes of three different spring constants, the same cantilevers used for imaging of the samples before and after loading. For calibration of the tip geometry, nanoindentation was performed on the bare PS film. The resulting elastic modulus values extracted by applying the Hertz model were 8.26 ± 3.43 GPa over the loaded micelles and 4.17 ± 1.65 GPa over the unloaded micelles, confirming that phase contrast images of a monolayer of loaded micelles represent maps of the nanoscale chemical and mechanical variation. By calibrating the tip geometry indirectly using a known soft material, we are able to use the same standard tapping mode cantilevers for both imaging and indentation.

Dynamics of epidemic spreading model with drug-resistant variation on scale-free networks

Science.gov (United States)

Wan, Chen; Li, Tao; Zhang, Wu; Dong, Jing

2018-03-01

Considering the influence of the virus' drug-resistant variation, a novel SIVRS (susceptible-infected-variant-recovered-susceptible) epidemic spreading model with variation characteristic on scale-free networks is proposed in this paper. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R0 and equilibriums are derived. Studies show that the existence of disease-free equilibrium is determined by the basic reproductive number R0. The relationships between the basic reproductive number R0, the variation characteristic and the topology of the underlying networks are studied in detail. Furthermore, our studies prove the global stability of the disease-free equilibrium, the permanence of epidemic and the global attractivity of endemic equilibrium. Numerical simulations are performed to confirm the analytical results.

Comparative analysis of face recognition techniques with illumination variation

International Nuclear Information System (INIS)

Jondhale, K C; Waghmare, L M

2010-01-01

Illumination variation is one of the major challenges in the face recognition. To deal with this problem, this paper presents comparative analysis of three different techniques. First, the DCT is employed to compensate for illumination variations in the logarithm domain. Since illumination variation lies mainly in the low frequency band, an appropriate number of DCT coefficients are truncated to reduce the variations under different lighting conditions. The nearest neighbor classifier based on Euclidean distance is employed for classification. Second, the performance of PCA is checked on normalized image. PCA is a technique used to reduce multidimensional data sets to a lower dimension for analysis. Third, LDA based methods gives a satisfactory result under controlled lighting condition. But its performance under large illumination variation is not satisfactory. So, the performance of LDA is checked on normalized image. Experimental results on the Yale B and ORL database show that the proposed approach of application of PCA and LDA on normalized dataset improves the performance significantly for the face images with large illumination variations.

JUPITER’S PHASE VARIATIONS FROM CASSINI : A TESTBED FOR FUTURE DIRECT-IMAGING MISSIONS

International Nuclear Information System (INIS)

Mayorga, L. C.; Jackiewicz, J.; Rages, K.; West, R. A.; Knowles, B.; Lewis, N.; Marley, M. S.

2016-01-01

We present empirical phase curves of Jupiter from ∼0° to 140° as measured in multiple optical bandpasses by Cassini /Imaging Science Subsystem (ISS) during the Millennium flyby of Jupiter in late 2000 to early 2001. Phase curves are of interest for studying the energy balance of Jupiter and understanding the scattering behavior of the planet as an exoplanet analog. We find that Jupiter is significantly darker at partial phases than an idealized Lambertian planet by roughly 25% and is not well fit by Jupiter-like exoplanet atmospheric models across all wavelengths. We provide analytic fits to Jupiter’s phase function in several Cassini /ISS imaging filter bandpasses. In addition, these observations show that Jupiter’s color is more variable with phase angle than predicted by models. Therefore, the color of even a near Jupiter-twin planet observed at a partial phase cannot be assumed to be comparable to that of Jupiter at full phase. We discuss how the Wide-Field Infrared Survey Telescope and other future direct-imaging missions can enhance the study of cool giants.

A variational integrators approach to second order modeling and identification of linear mechanical systems

NARCIS (Netherlands)

Bruschetta, M.; Saccon, A.; Picci, G.

2014-01-01

The theory of variational integration provides a systematic procedure to discretize the equations of motion of a mechanical system, preserving key properties of the continuous time flow. The discrete-time model obtained by variational integration theory inherits structural conditions which in

Modelling of classical ghost images obtained using scattered light

International Nuclear Information System (INIS)

Crosby, S; Castelletto, S; Aruldoss, C; Scholten, R E; Roberts, A

2007-01-01

The images obtained in ghost imaging with pseudo-thermal light sources are highly dependent on the spatial coherence properties of the incident light. Pseudo-thermal light is often created by reducing the coherence length of a coherent source by passing it through a turbid mixture of scattering spheres. We describe a model for simulating ghost images obtained with such partially coherent light, using a wave-transport model to calculate the influence of the scattering on initially coherent light. The model is able to predict important properties of the pseudo-thermal source, such as the coherence length and the amplitude of the residual unscattered component of the light which influence the resolution and visibility of the final ghost image. We show that the residual ballistic component introduces an additional background in the reconstructed image, and the spatial resolution obtainable depends on the size of the scattering spheres

Modelling of classical ghost images obtained using scattered light

Energy Technology Data Exchange (ETDEWEB)

Crosby, S; Castelletto, S; Aruldoss, C; Scholten, R E; Roberts, A [School of Physics, University of Melbourne, Victoria, 3010 (Australia)

2007-08-15

The images obtained in ghost imaging with pseudo-thermal light sources are highly dependent on the spatial coherence properties of the incident light. Pseudo-thermal light is often created by reducing the coherence length of a coherent source by passing it through a turbid mixture of scattering spheres. We describe a model for simulating ghost images obtained with such partially coherent light, using a wave-transport model to calculate the influence of the scattering on initially coherent light. The model is able to predict important properties of the pseudo-thermal source, such as the coherence length and the amplitude of the residual unscattered component of the light which influence the resolution and visibility of the final ghost image. We show that the residual ballistic component introduces an additional background in the reconstructed image, and the spatial resolution obtainable depends on the size of the scattering spheres.

An objective spinal motion imaging assessment (OSMIA): reliability, accuracy and exposure data.

Science.gov (United States)

Breen, Alan C; Muggleton, Jennifer M; Mellor, Fiona E

2006-01-04

Minimally-invasive measurement of continuous inter-vertebral motion in clinical settings is difficult to achieve. This paper describes the reliability, validity and radiation exposure levels in a new Objective Spinal Motion Imaging Assessment system (OSMIA) based on low-dose fluoroscopy and image processing. Fluoroscopic sequences in coronal and sagittal planes were obtained from 2 calibration models using dry lumbar vertebrae, plus the lumbar spines of 30 asymptomatic volunteers. Calibration model 1 (mobile) was screened upright, in 7 inter-vertebral positions. The volunteers and calibration model 2 (fixed) were screened on a motorized table comprising 2 horizontal sections, one of which moved through 80 degrees. Model 2 was screened during motion 5 times and the L2-S1 levels of the volunteers twice. Images were digitised at 5fps. Inter-vertebral motion from model 1 was compared to its pre-settings to investigate accuracy. For volunteers and model 2, the first digitised image in each sequence was marked with templates. Vertebrae were tracked throughout the motion using automated frame-to-frame registration. For each frame, vertebral angles were subtracted giving inter-vertebral motion graphs. Volunteer data were acquired twice on the same day and analysed by two blinded observers. The root-mean-square (RMS) differences between paired data were used as the measure of reliability. RMS difference between reference and computed inter-vertebral angles in model 1 was 0.32 degrees for side-bending and 0.52 degrees for flexion-extension. For model 2, X-ray positioning contributed more to the variance of range measurement than did automated registration. For volunteer image sequences, RMS inter-observer variation in intervertebral motion range in the coronal plane was 1.86 degrees and intra-subject biological variation was between 2.75 degrees and 2.91 degrees. RMS inter-observer variation in the sagittal plane was 1.94 degrees. Radiation dosages in each view were below

Probabilistic modelling in urban drainage – two approaches that explicitly account for temporal variation of model errors

DEFF Research Database (Denmark)

Löwe, Roland; Del Giudice, Dario; Mikkelsen, Peter Steen

of input uncertainties observed in the models. The explicit inclusion of such variations in the modelling process will lead to a better fulfilment of the assumptions made in formal statistical frameworks, thus reducing the need to resolve to informal methods. The two approaches presented here...

Reducing the variation in animal models by standardizing the gut microbiota

DEFF Research Database (Denmark)

Ellekilde, Merete; Hufeldt, Majbritt Ravn; Hansen, Camilla Hartmann Friis

2011-01-01

, a large proportion of laboratory animals are used to study such diseases, but inter-individual variation in these animal models leads to the need for larger group sizes to reach statistical significance and adequate power. By standardizing the microbial and immunological status of laboratory animals we...... mice changed the glucose tolerance without affecting weight or mucosal immunity. Further investigations concerning the mechanisms of how GM influences disease development is necessary, but based on these results it seems reasonable to assume that by manipulating the GM we may produce animal models...... may therefore be able to produce animals with a more standardized response and less variation. This would lead to more precise results and a reduced number of animals needed for statistical significance. Denaturing gradient gel electrophoresis (DGGE) - a culture independent approach separating PCR...

Image timing and detector performance of a matrix ion-chamber electronic portal imaging device

International Nuclear Information System (INIS)

Greer, P.

1996-01-01

The Oncology Centre of Auckland Hospital recently purchased a Varian PortalVision TM electronic portal imaging device (EPID). Image acquisition times, input-output characteristics and contrast-detail curves of this matrix liquid ion-chamber EPID have been measured to examine the variation in imaging performance with acquisition mode. The variation in detector performance with acquisition mode has been examined. The HV cycle time can be increased to improve image quality. Consideration should be given to the acquisition mode and HV cycle time used when imaging to ensure adequate imaging performance with reasonable imaging time. (author)

Modeling of electron time variations in the radiation belts

International Nuclear Information System (INIS)

Chan, K.W.; Teague, M.J.; Schofield, N.J.; Vette, J.I.

1979-01-01

A review of the temporal variation in the trapped electron population of the inner and outer radiation zones is presented. Techniques presently used for modeling these zones are discussed and their deficiencies identified. An intermediate region is indicated between the zones in which the present modeling techniques are inadequate due to the magnitude and frequency of magnetic storms. Future trends are examined, and it is suggested that modeling of individual magnetic storms may be required in certain L bands. An analysis of seven magnetic storms is presented, establishing the independence of the depletion time of the storm flux and the storm magnitude. Provisional correlation between the storm magnitude and the Dst index is demonstrated

Geometric Total Variation for Texture Deformation

DEFF Research Database (Denmark)

Bespalov, Dmitriy; Dahl, Anders Lindbjerg; Shokoufandeh, Ali

2010-01-01

In this work we propose a novel variational method that we intend to use for estimating non-rigid texture deformation. The method is able to capture variation in grayscale images with respect to the geometry of its features. Our experimental evaluations demonstrate that accounting for geometry...... of features in texture images leads to significant improvements in localization of these features, when textures undergo geometrical transformations. Accurate localization of features in the presense of unkown deformations is a crucial property for texture characterization methods, and we intend to expoit...

A variation method in the optimization problem of the minority game model

International Nuclear Information System (INIS)

Blazhyijevs'kij, L.; Yanyishevs'kij, V.

2009-01-01

This article contains the results of applying a variation method in the investigation of the optimization problem in the minority game model. That suggested approach is shown to give relevant results about phase transition in the model. Other methods pertinent to the problem have also been assessed.

Delay Variation Model with RTP Flows Behavior in Accordance with M/D/1 Kendall's Notation

Directory of Open Access Journals (Sweden)

Miroslav Voznak

2010-01-01

Full Text Available This paper focuses on the design of a mathematical model of end-to-end delay of a VoIP connection, in particular on a delay variation. It describes all partial delay components and mechanisms, its generation, facilities and its mathematical formulations. A new approach to the delay variation model is presented; its validation has been done by an experiment.

Generalized PSF modeling for optimized quantitation in PET imaging.

Science.gov (United States)

Ashrafinia, Saeed; Mohy-Ud-Din, Hassan; Karakatsanis, Nicolas A; Jha, Abhinav K; Casey, Michael E; Kadrmas, Dan J; Rahmim, Arman

2017-06-21

Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important. Frameworks explored in the past involved a dichotomy of PSF versus no-PSF modeling. By contrast, the present work focuses on quantitative performance evaluation of standard uptake value (SUV) PET images, while incorporating a wide spectrum of PSF models, including those that under- and over-estimate the true PSF, for the potential of enhanced quantitation of SUVs. The developed framework first analytically models the true PSF, considering a range of resolution degradation phenomena (including photon non-collinearity, inter-crystal penetration and scattering) as present in data acquisitions with modern commercial PET systems. In the context of oncologic liver FDG PET imaging, we generated 200 noisy datasets per image-set (with clinically realistic noise levels) using an XCAT anthropomorphic phantom with liver tumours of varying sizes. These were subsequently reconstructed using the OS-EM algorithm with varying PSF modelled kernels. We focused on quantitation of both SUV mean and SUV max , including assessment of contrast recovery coefficients, as well as noise-bias characteristics (including both image roughness and coefficient of-variability), for different tumours/iterations/PSF kernels. It was observed that overestimated PSF yielded more accurate contrast recovery for a range of tumours, and typically improved quantitative performance. For a clinically reasonable number of iterations, edge enhancement due to PSF modeling (especially due to over-estimated PSF) was in fact seen to lower SUV mean bias in small tumours. Overall, the results indicate that exactly matched PSF

Modeling the National Ignition Facility neutron imaging system.

Science.gov (United States)

Wilson, D C; Grim, G P; Tregillis, I L; Wilke, M D; Patel, M V; Sepke, S M; Morgan, G L; Hatarik, R; Loomis, E N; Wilde, C H; Oertel, J A; Fatherley, V E; Clark, D D; Fittinghoff, D N; Bower, D E; Schmitt, M J; Marinak, M M; Munro, D H; Merrill, F E; Moran, M J; Wang, T-S F; Danly, C R; Hilko, R A; Batha, S H; Frank, M; Buckles, R

2010-10-01

Numerical modeling of the neutron imaging system for the National Ignition Facility (NIF), forward from calculated target neutron emission to a camera image, will guide both the reduction of data and the future development of the system. Located 28 m from target chamber center, the system can produce two images at different neutron energies by gating on neutron arrival time. The brighter image, using neutrons near 14 MeV, reflects the size and symmetry of the implosion "hot spot." A second image in scattered neutrons, 10-12 MeV, reflects the size and symmetry of colder, denser fuel, but with only ∼1%-7% of the neutrons. A misalignment of the pinhole assembly up to ±175 μm is covered by a set of 37 subapertures with different pointings. The model includes the variability of the pinhole point spread function across the field of view. Omega experiments provided absolute calibration, scintillator spatial broadening, and the level of residual light in the down-scattered image from the primary neutrons. Application of the model to light decay measurements of EJ399, BC422, BCF99-55, Xylene, DPAC-30, and Liquid A suggests that DPAC-30 and Liquid A would be preferred over the BCF99-55 scintillator chosen for the first NIF system, if they could be fabricated into detectors with sufficient resolution.