Multi-method 2D and 3D reconstruction of gold grains morphology in alluvial deposits: a review and application to the Rivière du Moulin (Québec, Canada)

2021 ◽  
pp. SP516-2020-186
Author(s):  
François-Xavier Masson ◽  
Georges Beaudoin ◽  
Denis Laurendeau
Keyword(s):  
Quantitative Methods ◽  
Large Set ◽  
Shape Descriptors ◽  
Analysis Software ◽  
Alluvial Deposits ◽  
Image Analysis Software ◽  
Fluvial Transport ◽  
The Difference ◽  
3D Methods ◽  
2D And 3D

AbstractThe aim of this paper is to document and compare the 2D qualitative and semi-quantitative methods currently used to describe the shape of gold grains in fluvial environment to 3D quantitative methods using microtomography and SEM photogrammetry. These 3D methods are used to compute flatness, roundness, convexity, sphericity, and ellipticity shape descriptors of 13 gold grains from the Rivière du Moulin (Québec, Canada) in order to quantify the morphological change along 9 km of fluvial transport. Gold grains have moderate to high values of flatness, compactness, sphericity, and ellipticity indices that do not change significantly with distance of transport, whereas the roundness increases during transport. Gold grains are used to compare 2D and 3D methods and the results show small differences (< 8%) when shape descriptors are computed using image analysis software, whereas the difference (up to 70%) is more important for 2D measurements performed by a human operator. For application and characterization on a large set of gold grains, the 2D methods offer the advantage of speed, whereas, for a more detailed study on a limited number of gold grains, 3D methods enable estimation of the volume and yield more detailed shape descriptors changes during fluvial transport.

scholarly journals Comparison of Inoculation with the InoqulA and WASP Automated Systems with Manual Inoculation

2015 ◽  
Vol 53 (7) ◽  
pp. 2298-2307 ◽  
Author(s):  
Antony Croxatto ◽  
Klaas Dijkstra ◽  
Guy Prod'hom ◽  
Gilbert Greub
Keyword(s):  
Susceptibility Testing ◽  
Bacterial Species ◽  
Automated Systems ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Chromogenic Agar ◽  
Manual Methods ◽  
The Difference ◽  
Urine Specimens

The quality of sample inoculation is critical for achieving an optimal yield of discrete colonies in both monomicrobial and polymicrobial samples to perform identification and antibiotic susceptibility testing. Consequently, we compared the performance between the InoqulA (BD Kiestra), the WASP (Copan), and manual inoculation methods. Defined mono- and polymicrobial samples of 4 bacterial species and cloudy urine specimens were inoculated on chromogenic agar by the InoqulA, the WASP, and manual methods. Images taken with ImagA (BD Kiestra) were analyzed with the VisionLab version 3.43 image analysis software to assess the quality of growth and to prevent subjective interpretation of the data. A 3- to 10-fold higher yield of discrete colonies was observed following automated inoculation with both the InoqulA and WASP systems than that with manual inoculation. The difference in performance between automated and manual inoculation was mainly observed at concentrations of >106bacteria/ml. Inoculation with the InoqulA system allowed us to obtain significantly more discrete colonies than the WASP system at concentrations of >107bacteria/ml. However, the level of difference observed was bacterial species dependent. Discrete colonies of bacteria present in 100- to 1,000-fold lower concentrations than the most concentrated populations in defined polymicrobial samples were not reproducibly recovered, even with the automated systems. The analysis of cloudy urine specimens showed that InoqulA inoculation provided a statistically significantly higher number of discrete colonies than that with WASP and manual inoculation. Consequently, the automated InoqulA inoculation greatly decreased the requirement for bacterial subculture and thus resulted in a significant reduction in the time to results, laboratory workload, and laboratory costs.

scholarly journals The MITK Approach

2005 ◽  
Author(s):  
Ivo Wolf ◽  
Marco Nolden ◽  
Thomas Boettger ◽  
Ingmar Wegner ◽  
Max Schoebinger ◽  
...  
Keyword(s):  
Image Analysis ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Interactive Systems ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Visualization Toolkit ◽  
Key Features ◽  
2D And 3D

The Medical Imaging Interaction Toolkit (MITK) is an opensource toolkit for the development of interactive medical image analysis software. MITK is based on the open-source Insight Toolkit (ITK) and Visualization Toolkit (VTK) and extends them with features required for interactive systems. ITK is used for the algorithmic scope and general infrastructure, VTK for visualization. Key features of MITK are the coordination of multiple 2D and 3D visualizations of arbitrary data, a general interaction concept including undo/redo, and its extendibility and flexibility to create tailored applications due to its toolkit character and different layers of hidden complexity. The paper gives a brief introduction into the overall concepts and goals of the MITK approach. Suggestions and participation are welcome. MITK is available at www.mitk.org.

scholarly journals Computer-Aided 2D and 3D quantification of human stem cell fate from in vitro samples using Volocity high performance image analysis software

2011 ◽  
Vol 7 (3) ◽  
pp. 256-263 ◽  
Author(s):  
Katja M. Piltti ◽  
Daniel L. Haus ◽  
Eileen Do ◽  
Harvey Perez ◽  
A.J. Anderson ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cell Fate ◽  
High Performance ◽  
Human Stem Cell ◽  
Analysis Software ◽  
Stem Cell Fate ◽  
Image Analysis Software ◽  
Computer Aided ◽  
2D And 3D

Learning Language Levels in Students Accurate with a History Academic Achievement History

2017 ◽  
Vol 1 (2) ◽  
pp. 137
Author(s):  
Noorlela Binti Noordin ◽  
Abdul Razaq Ahmad ◽  
Anuar Ahmad
Keyword(s):  
Academic Achievement ◽  
Statistical Inference ◽  
Quantitative Data ◽  
Quantitative Methods ◽  
Descriptive Statistics ◽  
Language Test ◽  
The Subject ◽  
The Difference ◽  
Learning Language ◽  
The Relationship

This study was aimed to evaluate the Malay proficiency among students in Form Two especially non-Malay students and its relationship to academic achievement History. To achieve the purpose of the study there are two objectives, the first is to look at the difference between mean of Malay Language test influences min of academic achievement of History subject among non-Malay students in Form Two and the second is the relationship between the level of Malay proficiency and their academic achievement for History. This study used quantitative methods, which involved 100 people of Form Two non-Malay students in one of the schools in Klang, Selangor. This study used quantitative data were analyzed using descriptive statistics and statistical inference with IBM SPSS Statistics v22 software. This study found that there was a relationship between the proficiency of Malay language among non-Malay students with achievements in the subject of History. The implications of this study are discussed in this article.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Image analysis software and sample preparation demands

1990 ◽  
Author(s):  
Karl n. Roth ◽  
Knut Wenzelides ◽  
Guenter Wolf ◽  
Peter Hufnagl
Keyword(s):  
Image Analysis ◽  
Sample Preparation ◽  
Analysis Software ◽  
Image Analysis Software
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