Adaptive and quality 3D meshing from imaging data

2003 ◽  
Author(s):  
Yongjie Zhang ◽  
Chandrajit Bajaj ◽  
Bong-Soo Sohn
Keyword(s):  
Imaging Data ◽  
3D Meshing

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

Cleavage of Human Embryos: Options and Diversity

Acta Naturae ◽  
2016 ◽  
Vol 8 (3) ◽  
pp. 88-96
Author(s):  
Yu. K. Doronin ◽  
I. V. Senechkin ◽  
L. V. Hilkevich ◽  
M. A. Kurcer
Keyword(s):  
Time Lapse ◽  
Reproductive Technologies ◽  
Human Embryos ◽  
Imaging Data ◽  
Noninvasive Methods ◽  
Topographic Features ◽  
Time Parameters ◽  
Embryo Cleavage ◽  
Time Lapse Imaging ◽  
Developmental Dynamics

In order to estimate the diversity of embryo cleavage relatives to embryo progress (blastocyst formation), time-lapse imaging data of preimplantation human embryo development were used. This retrospective study is focused on the topographic features and time parameters of the cleavages, with particular emphasis on the lengths of cleavage cycles and the genealogy of blastomeres in 2- to 8-cell human embryos. We have found that all 4-cell human embryos have four developmental variants that are based on the sequence of appearance and orientation of cleavage planes during embryo cleavage from 2 to 4 blastomeres. Each variant of cleavage shows a strong correlation with further developmental dynamics of the embryos (different cleavage cycle characteristics as well as lengths of blastomere cycles). An analysis of the sequence of human blastomere divisions allowed us to postulate that the effects of zygotic determinants are eliminated as a result of cleavage, and that, thereafter, blastomeres acquire the ability of own syntheses, regulation, polarization, formation of functional contacts, and, finally, of specific differentiation. This data on the early development of human embryos obtained using noninvasive methods complements and extend our understanding of the embryogenesis of eutherian mammals and may be applied in the practice of reproductive technologies.

Articulatory Synthesis Based on Real-Time Magnetic Resonance Imaging Data

2016 ◽  
Author(s):  
Asterios Toutios ◽  
Tanner Sorensen ◽  
Krishna Somandepalli ◽  
Rachel Alexander ◽  
Shrikanth S. Narayanan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Real Time ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Data ◽  
Articulatory Synthesis

[18F]-florbetaben PET/CT Imaging in the Alzheimer’s Disease Mouse Model APPswe/PS1dE9

2018 ◽  
Vol 16 (1) ◽  
pp. 49-55 ◽  
Author(s):  
J. Stenzel ◽  
C. Rühlmann ◽  
T. Lindner ◽  
S. Polei ◽  
S. Teipel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
New Drugs ◽  
Imaging Data ◽  
Wild Type ◽  
Preclinical Research ◽  
Standardized Uptake Values ◽  
Pet Ct

Background: Positron-emission-tomography (PET) using 18F labeled florbetaben allows noninvasive in vivo-assessment of amyloid-beta (Aβ), a pathological hallmark of Alzheimer’s disease (AD). In preclinical research, [<sup>18</sup>F]-florbetaben-PET has already been used to test the amyloid-lowering potential of new drugs, both in humans and in transgenic models of cerebral amyloidosis. The aim of this study was to characterize the spatial pattern of cerebral uptake of [<sup>18</sup>F]-florbetaben in the APPswe/ PS1dE9 mouse model of AD in comparison to histologically determined number and size of cerebral Aβ plaques. Methods: Both, APPswe/PS1dE9 and wild type mice at an age of 12 months were investigated by smallanimal PET/CT after intravenous injection of [<sup>18</sup>F]-florbetaben. High-resolution magnetic resonance imaging data were used for quantification of the PET data by volume of interest analysis. The standardized uptake values (SUVs) of [<sup>18</sup>F]-florbetaben in vivo as well as post mortem cerebral Aβ plaque load in cortex, hippocampus and cerebellum were analyzed. Results: Visual inspection and SUVs revealed an increased cerebral uptake of [<sup>18</sup>F]-florbetaben in APPswe/ PS1dE9 mice compared with wild type mice especially in the cortex, the hippocampus and the cerebellum. However, SUV ratios (SUVRs) relative to cerebellum revealed only significant differences in the hippocampus between the APPswe/PS1dE9 and wild type mice but not in cortex; this differential effect may reflect the lower plaque area in the cortex than in the hippocampus as found in the histological analysis. Conclusion: The findings suggest that histopathological characteristics of Aβ plaque size and spatial distribution can be depicted in vivo using [<sup>18</sup>F]-florbetaben in the APPswe/PS1dE9 mouse model.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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