Fine-Aggregate Angularity: Automated Image Analysis Approach

2000 ◽  
Vol 1721 (1) ◽  
pp. 66-72 ◽  
Author(s):  
Eyad Masad ◽  
Joe W. Button ◽  
Tom Papagiannakis
Keyword(s):  
Image Analysis ◽  
Visual Analysis ◽  
Imaging Techniques ◽  
Permanent Deformation ◽  
Asphalt Mixtures ◽  
Automated Image Analysis ◽  
Fine Aggregate ◽  
Smoothing Effect ◽  
Aggregate Properties ◽  
Wide Range

Angularity is one of the important aggregate properties contributing to the permanent deformation resistance of asphalt mixtures. Therefore, methods that are able to rapidly and accurately describe aggregate angularity are valuable in the design process of asphalt mixtures. Two computer-automated procedures, which make use of the advances in digital-image processing, to quantify fine aggregate angularity, are presented. The first method relies on the concepts of the erosion-dilation techniques. This consists of subjecting the aggregate surface to a smoothing effect that causes the angularity elements to disappear from the image. Then, the area lost as a result of the smoothing effect is calculated and used to quantify angularity. The second method is based on the fractal approach. Image-analysis techniques are used to measure the fractal length of aggregate boundary. The fractal length increases with aggregate angularity. The proposed imaging techniques are used to capture the aggregate angularity of 23 sand samples that represent a wide range of materials. The results are compared with visual analysis and indirect methods of measuring fine-aggregate angularity, such as the uncompacted air voids, and the angle of internal friction of aggregate mass. In general, the results indicate much promise for measuring aggregate properties using automated imaging technologies.

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

Two-dimensional impurity imaging in polar ice cores sparks new demand for automated image analysis

2021 ◽  
Author(s):  
Pascal Bohleber ◽  
Marco Roman ◽  
Sebastiano Vascon ◽  
Marcello Pelillo ◽  
Carlo Barbante
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Inductively Coupled Plasma ◽  
Visual Analysis ◽  
Ice Core ◽  
Ice Cores ◽  
Sample Surface ◽  
Automated Image Analysis ◽  
Polar Ice ◽  
Icp Ms

<p>Due to its micron-scale resolution and micro-destructiveness, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is especially suited for the analysis of the oldest and highly thinned sections of polar ice cores. State-of-the-art 2D elemental imaging by LA-ICP-MS has great potential for investigating the location of impurities on the ice sample surface and is crucial to avoid misinterpretation of ultra-fine resolution signals. The impurity imaging with LA-ICP-MS comprises several millions of laser shots fired over just a few square mm. This technique combines new chemical images with visual analysis and, in so doing raises new questions that may be answered through techniques in automated image analysis and computer vision. As an illustration of this new frontier, a selected set of key problems is presented, with first examples of how automated image analysis techniques can help solving them. This concerns the relationship between impurity localization and the grain boundary network as well as the paleoclimate significance of single line profiles along the main core axis. Ultimately, this demonstrates that it is the right time to spark an intensified exchange among the two scientific communities of computer vision and ice core science.</p>

scholarly journals The radiological diagnosis of bronchiectasis: what's in a name?

2020 ◽  
Vol 29 (156) ◽  
pp. 190120
Author(s):  
Harm A.W.M. Tiddens ◽  
Jennifer J. Meerburg ◽  
Menno M. van der Eerden ◽  
Pierluigi Ciet
Keyword(s):  
Image Analysis ◽  
Lung Volume ◽  
State Of The Art ◽  
Imaging Techniques ◽  
Automated Image Analysis ◽  
Radiological Diagnosis ◽  
Specific Reference ◽  
Airway Wall ◽  
Gold Standard Method ◽  
Airway Dimensions

Diagnosis of bronchiectasis is usually made using chest computed tomography (CT) scan, the current gold standard method. A bronchiectatic airway can show abnormal widening and thickening of its airway wall. In addition, it can show an irregular wall and lack of tapering, and/or can be visible in the periphery of the lung. Its diagnosis is still largely expert based. More recently, it has become clear that airway dimensions on CT and therefore the diagnosis of bronchiectasis are highly dependent on lung volume. Hence, control of lung volume is required during CT acquisition to standardise the evaluation of airways. Automated image analysis systems are in development for the objective analysis of airway dimensions and for the diagnosis of bronchiectasis. To use these systems, clear and objective definitions for the diagnosis of bronchiectasis are needed. Furthermore, the use of these systems requires standardisation of CT protocols and of lung volume during chest CT acquisition. In addition, sex- and age-specific reference values are needed for image analysis outcome parameters. This review focusses on today's issues relating to the radiological diagnosis of bronchiectasis using state-of-the-art CT imaging techniques.

scholarly journals Image analysis of bromodeoxyuridine (BrdU) staining for measurement of S-phase in rat and mouse liver.

1994 ◽  
Vol 42 (7) ◽  
pp. 939-944 ◽  
Author(s):  
A R Soames ◽  
D Lavender ◽  
J R Foster ◽  
S M Williams ◽  
E B Wheeldon
Keyword(s):  
Image Analysis ◽  
Mouse Liver ◽  
Form Factors ◽  
Automated Image Analysis ◽  
Automated System ◽  
Image Analysis System ◽  
Intense Staining ◽  
Wide Range ◽  
Analysis System ◽  
Brdu Staining

We developed a system for quantifying the numbers of bromodeoxyuridine (BrdU)-labeled hepatocyte nuclei in rat and mouse liver with an automated image analysis system. We began by developing a protocol for BrdU staining that would provide consistently intense staining to facilitate identification of both labeled and unlabeled nuclei by image analysis. Preliminary studies detected and characterized hepatocyte nuclei and differentiated them from non-hepatocyte nuclei using area and form factors. The parameters were selected to optimize discrimination between the two populations, selecting 90% of hepatocyte and 5% non-hepatocyte nuclei. Finally, we developed a program for automatic counting of BrdU-labeled hepatocyte and total hepatocyte nuclei. Results obtained from this method correlated well with data collected by a microscopist over a wide range of labeling indices. The automated system reduces interobserver variation and should minimize intraobserver error, as well as reducing the tedium of measuring labeling indices in the liver. Moreover, the techniques described should be applicable to other tissues.

An Overview of Moisture Damage in Asphalt Mixtures

2015 ◽  
Vol 73 (4) ◽  
Author(s):  
Meor Othman Hamzah ◽  
Muhammad Rafiq Kakar ◽  
Mohd Rosli Hainin
Keyword(s):  
Permanent Deformation ◽  
Asphalt Mixture ◽  
Material Design ◽  
Damage Mechanism ◽  
Moisture Damage ◽  
Short Review ◽  
Testing Procedure ◽  
Asphalt Mixtures ◽  
Test Method ◽  
Wide Range

This paper presents a short review on moisture induced damage in asphalt mixtures. Moisture induced damage is one of the most common causes of pavement distress that results in loss of strength, stripping, raveling, fatigue damage and permanent deformation. Different mechanisms have been used to explain the process of moisture damage in asphalt pavements. However, the moisture damage mechanism takes place due to the interaction of several different processes. The applicability of a single test method to evaluate moisture damage is impractical to a wide range of materials and conditions. Therefore, a new laboratory based testing procedure and analysis protocol is required, with the aim to simultaneously consider the effects of both traffic impact and moisture damage. The proper material design, efficient construction methods, reliable laboratory techniques and well planned highway surface and subsurface drainage systems may lead towards a sustainable asphalt pavement that is sufficiently durable to resist moisture damage. Although considerable advances concerning the subject have been reported, yet there is still a need to address certain issues that are actually involved in the process of asphalt mixture moisture susceptibility.

Automation strategies for large-scale 3D image analysis

2016 ◽  
Vol 64 (7) ◽  
Author(s):  
Johannes Stegmaier ◽  
Benjamin Schott ◽  
Eduard Hübner ◽  
Manuel Traub ◽  
Maryam Shahid ◽  
...  
Keyword(s):  
Image Analysis ◽  
Large Scale ◽  
Imaging Techniques ◽  
Image Data ◽  
Automated Image Analysis ◽  
Future Research ◽  
Time Resolved ◽  
Processing Strategies ◽  
Spatio Temporal ◽  
Automation Strategies

AbstractNew imaging techniques enable visualizing and analyzing a multitude of unknown phenomena in many areas of science at high spatio-temporal resolution. The rapidly growing amount of image data, however, can hardly be analyzed manually and, thus, future research has to focus on automated image analysis methods that allow one to reliably extract the desired information from large-scale multidimensional image data. Starting with infrastructural challenges, we present new software tools, validation benchmarks and processing strategies that help coping with large-scale image data. The presented methods are illustrated on typical problems observed in developmental biology that can be answered, e.g., by using time-resolved 3D microscopy images.

Characteristics of Typical Minnesota Aggregates

1997 ◽  
Vol 1583 (1) ◽  
pp. 1-10
Author(s):  
M. Stroup-Gardiner ◽  
D. Newcomb ◽  
W. Kussman ◽  
Roger Olson
Keyword(s):  
Construction Projects ◽  
Coarse Aggregate ◽  
Fine Aggregate ◽  
Moisture Sensitivity ◽  
Air Voids ◽  
Aggregate Properties ◽  
Wide Range ◽  
Aggregate Fractions ◽  
Pass Through ◽  
Single Regression

The consensus aggregate properties recommended in SUPERPAVE and selected mixture properties were evaluated for a wide range of Minnesota aggregate sources obtained from 16 construction projects completed in 1993. Measured aggregate properties included the sand equivalent (SE) and fine aggregate angularity for the fine aggregate fractions, and percentage of fractured faces and flat and elongated particles in the coarse aggregate fractions. Laboratory-compacted samples were prepared and tested to determine air voids, voids in mineral aggregate (VMA), tensile strengths, and an assessment of the moisture sensitivity of the mixtures. Only 3 of 29 SE values for individual stockpiles were less than 40 percent. These values were not significantly affected by changes in either the general mineralogy (i.e., igneous, limestone, mixed) or the percentage passing the 0.075-mm (No. 200) sieve. Single regression analyses indicated no significant relationship between SE and either mixture moisture sensitivity or VMA. While all 25 stockpiles tested had fine aggregate angularity values greater than 40, 9 stockpiles had values below 45. It was suggested that since Minnesota aggregate gradations commonly pass through the SUPERPAVE re-stricted zone (one purpose of which is to limit the use of rounded natural sands), the minimum fine aggregate angularity value be set at 45 for all mixtures to preclude the use of 100 percent natural sands. A significant number of Minnesota coarse aggregate stockpiles have a moderate to high content of flat particles (20 to 50 percent).

scholarly journals MIA: An Open Source Standalone Deep Learning Application for Microscopic Image Analysis

2022 ◽  
Author(s):  
Nils Koerber
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Open Source ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Microscopic Image ◽  
Biomedical Sciences ◽  
Model Training

In recent years the amount of data generated by imaging techniques has grown rapidly along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, we present the Microscopic Image Analyzer (MIA). MIA combines a graphical user interface that obviates the need for programming skills with state-of-the-art deep learning algorithms for segmentation, object detection, and classification. It runs as a standalone, platform-independent application and is compatible with commonly used open source software packages. The software provides a unified interface for easy image labeling, model training and inference. Furthermore the software was evaluated in a public competition and performed among the top three for all tested data sets. The source code is available on https://github.com/MIAnalyzer/MIA.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

Progress In The Practical Application Of Automated Image Analysis To Tem Negatives

1977 ◽  
Vol 35 ◽  
pp. 214-217
Author(s):  
F. A. Heckman ◽  
E. Redman ◽  
J.E. Connolly
Keyword(s):  
Image Analysis ◽  
Image Quality ◽  
Exposure Time ◽  
Image Contrast ◽  
Automated Image Analysis ◽  
Practical Application ◽  
Factors Affecting ◽  
High Image Quality ◽  
Qualitative Evidence ◽  
Comprehensive Study

In our initial publication on this subject1) we reported results demonstrating that contrast is the most important factor in producing the high image quality required for reliable image analysis. We also listed the factors which enhance contrast in order of the experimentally determined magnitude of their effect. The two most powerful factors affecting image contrast attainable with sheet film are beam intensity and KV. At that time we had only qualitative evidence for the ranking of enhancing factors. Later we carried out the densitometric measurements which led to the results outlined below.Meaningful evaluations of the cause-effect relationships among the considerable number of variables in preparing EM negatives depend on doing things in a systematic way, varying only one parameter at a time. Unless otherwise noted, we adhered to the following procedure evolved during our comprehensive study:Philips EM-300; 30μ objective aperature; magnification 7000- 12000X, exposure time 1 second, anti-contamination device operating.

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