scholarly journals High resolution nonlinear registration with simultaneous modelling of intensities

2019 ◽  
Author(s):  
Jesper L. R. Andersson ◽  
Mark Jenkinson ◽  
Stephen Smith
Keyword(s):  
Experimental Data ◽  
High Resolution ◽  
Image Registration ◽  
Cost Function ◽  
Small Deformation ◽  
Brain Regions ◽  
Software Library ◽  
Joint Shape ◽  
Nonlinear Registration ◽  
Intensity Model

AbstractThis paper describes and evaluates FMRIB’s nonlinear image registration tool (FNIRT), that is part of the FMRIB software library (FSL). It is a small deformation framework using sum of squared differences (SSD) as its cost function and Gauss-Newton for minimisation. The framework uses a joint shape and intensity model that attempts to explain the observed differences between two images in terms of having different shape and/or contrast, being differently affected by intensity bias-fields etc. Thus the estimation of the warps will be relatively unaffected by intensity differences that would otherwise violate the assumptions behind the SSD cost function. It uses a projection onto a manifold defined by a specified range of allowed Jacobian determinants to ensure that the warps are diffeomorphic. The utility of the model is demonstrated on a variety of simulated and experimental data with good results. FNIRT is also quantitatively evaluated using previously published datasets consisting of scans from multiple subjects, all with anatomically defined brain regions that are manually outlined. In this evaluation FNIRT performs well in comparison to previously published results with other registration algorithms.

Computing the Medical Image Registration Using Meta-Heuristics

2014 ◽  
Vol 643 ◽  
pp. 237-242 ◽  
Author(s):  
Tahari Abdou El Karim ◽  
Bendakmousse Abdeslam ◽  
Ait Aoudia Samy
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Image Registration ◽  
Cost Function ◽  
Population Genetic ◽  
Medical Image ◽  
Medical Image Registration ◽  
Anatomical Structures

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

Experimental and Simulation of a Cam and Translated Roller Follower Over a Range of Speeds

2016 ◽  
Author(s):  
Louay S. Yousuf ◽  
Dan B. Marghitu
Keyword(s):  
Experimental Data ◽  
Lyapunov Exponent ◽  
High Resolution ◽  
Lyapunov Exponents ◽  
Planar Motion ◽  
Largest Lyapunov Exponents ◽  
Set Up ◽  
The Impact ◽  
Impact With Friction

In this study a cam and follower mechanism is analyzed. There is a clearance between the follower and the guide. The mechanism is analyzed using SolidWorks simulations taking into account the impact and the friction between the roller follower and the guide. Four different follower guide’s clearances have been used in the simulations like 0.5, 1, 1.5, and 2 mm. An experimental set up is developed to capture the general planar motion of the cam and follower. The measures of the cam and the follower positions are obtained through high-resolution optical encoders (markers). The effect of follower guide’s clearance is investigated for different cam rotational speeds such as 100, 200, 300, 400, 500, 600, 700 and 800 R.P.M. Impact with friction is considered in our study to calculate the Lyapunov exponent. The largest Lyapunov exponents for the simulated and experimental data are analyzed and selected.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

scholarly journals Automatic Segmentation of the Dorsal Claustrum in Humans Using in vivo High-Resolution MRI

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Shai Berman ◽  
Roey Schurr ◽  
Gal Atlan ◽  
Ami Citri ◽  
Aviv A Mezer
Keyword(s):  
High Resolution ◽  
Automatic Segmentation ◽  
Thin Sheet ◽  
Brain Regions ◽  
Reproducible Research ◽  
Human Connectome Project ◽  
High Resolution Mri ◽  
Magnetic Resonance Imaging Mri ◽  
And Function

Abstract The claustrum is a thin sheet of neurons enclosed by white matter and situated between the insula and the putamen. It is highly interconnected with sensory, frontal, and subcortical regions. The deep location of the claustrum, with its fine structure, has limited the degree to which it could be studied in vivo. Particularly in humans, identifying the claustrum using magnetic resonance imaging (MRI) is extremely challenging, even manually. Therefore, automatic segmentation of the claustrum is an invaluable step toward enabling extensive and reproducible research of the anatomy and function of the human claustrum. In this study, we developed an automatic algorithm for segmenting the human dorsal claustrum in vivo using high-resolution MRI. Using this algorithm, we segmented the dorsal claustrum bilaterally in 1068 subjects of the Human Connectome Project Young Adult dataset, a publicly available high-resolution MRI dataset. We found good agreement between the automatic and manual segmentations performed by 2 observers in 10 subjects. We demonstrate the use of the segmentation in analyzing the covariation of the dorsal claustrum with other brain regions, in terms of macro- and microstructure. We identified several covariance networks associated with the dorsal claustrum. We provide an online repository of 1068 bilateral dorsal claustrum segmentations.

Multilevel similarity model for high-resolution remote sensing image registration

2019 ◽  
Vol 505 ◽  
pp. 294-305 ◽  
Author(s):  
Xianmin Wang ◽  
Jing Li ◽  
Jin Li ◽  
Hongyang Yan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Similarity Model

scholarly journals Identification and Distribution of Novel Metabolites of Lolitrem B in Mice by High-Resolution Mass Spectrometry

Molecules ◽  
2020 ◽  
Vol 25 (2) ◽  
pp. 372 ◽  
Author(s):  
Priyanka Reddy ◽  
Aaron Elkins ◽  
Joanne Hemsworth ◽  
Kathryn Guthridge ◽  
Simone Vassiliadis ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
Brain Regions ◽  
High Resolution Mass ◽  
Metabolic Products ◽  
Severe Intoxication ◽  
Lolitrem B ◽  
Resolution Mass

Lolitrem B is the most potent indole-diterpene mycotoxin produced by Epichloë festucae var. lolii (termed LpTG-1), with severe intoxication cases reported in livestock. To date, there are no in vivo metabolism studies conducted for the mycotoxin. A mouse model assay established for assessing toxicity of indole-diterpenes was used to investigate metabolic products of lolitrem B. Mice were administered lolitrem B at 0.5 and 2.0 mg/kg body weight (b.wt) intraperitoneally before body and brain tissues were collected at 6 h and 24 h post-treatment. Samples were cryoground and subjected to a biphasic or monophasic extraction. The aqueous and lipophilic phases were analysed using liquid chromatography high-resolution mass spectrometry (LC–HRMS); data analysis was performed with Compound Discoverer™ software. A total of 10 novel phase I metabolic products were identified in the lipophilic phase and their distribution in the liver, kidney and various brain regions are described. The biotransformation products of lolitrem B were found to be present in low levels in the brain. Based on structure–activity postulations, six of these may contribute towards the protracted tremors exhibited by lolitrem B-exposed animals.

scholarly journals High-Resolution Infrared Spectroscopy of the v1+ v4Band of14NF3:Reductions of the Rovibrational Hamiltonian

2012 ◽  
Vol 9 (1) ◽  
pp. 253-259 ◽  
Author(s):  
Hamid Najib ◽  
Siham Hmimou ◽  
Hicham Msahal
Keyword(s):  
Experimental Data ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Excited State ◽  
High Resolution ◽  
Infrared Spectrum ◽  
Fourier Transform Infrared ◽  
Sixth Order ◽  
Degenerate State ◽  
Unitary Equivalence

The high-resolution Fourier transform infrared spectrum of nitrogen trifluoride NF3has been studied in the v1+ v4perpendicular band region around 1523 cm−1. All experimental data have been refined applying various reduction forms of the effective rovibrational Hamiltonian developed for an isolated degenerate state of a symmetric top molecule. The v1= v4= 1 excited state of the14NF3oblate molecule was treated with models taking into account ℓ- andk-type intravibrational resonances. Parameters up to sixth order have been accurately determined and the unitary equivalence of the derived parameter sets in different reductions was demonstrated.

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