scholarly journals Automated image analysis methods for 3-D quantification of the neurovascular unit from multichannel confocal microscope images

2005 ◽  
Vol 66A (1) ◽  
pp. 9-23 ◽  
Author(s):  
Gang Lin ◽  
Chris S. Bjornsson ◽  
Karen L. Smith ◽  
Muhammad-Amri Abdul-Karim ◽  
James N. Turner ◽  
...  
Keyword(s):  
Image Analysis ◽  
Neurovascular Unit ◽  
Confocal Microscope ◽  
Automated Image Analysis ◽  
Analysis Methods ◽  
Microscope Images

scholarly journals Software for quantification of labeled bacteria from digital microscope images by automated image analysis

BioTechniques ◽  
2005 ◽  
Vol 39 (6) ◽  
pp. 859-863 ◽  
Author(s):  
Jyrki Selinummi ◽  
Jenni Seppälä ◽  
Olli Yli-Harja ◽  
Jaakko A. Puhakka
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Microscope Images ◽  
Digital Microscope

COMPARISON OF SEGMENTATION METHODS FOR DIGITAL IMAGE ANALYSIS OF CONFOCAL MICROSCOPE IMAGES TO MEASURE TRACHEID CELL DIMENSIONS

IAWA Journal ◽  
2001 ◽  
Vol 22 (3) ◽  
pp. 267-288 ◽  
Author(s):  
Mattias K. Moëll ◽  
Lloyd A. Donaldson
Keyword(s):  
Cell Wall ◽  
Image Analysis ◽  
Digital Image ◽  
Automatic Segmentation ◽  
Transverse Cross Section ◽  
Confocal Microscope ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Cell Dimensions ◽  
Microscope Images

Image analysis is a common tool for measuring tracheid cell dimensions. When analyzing a digital image of a transverse cross section of wood, one of the initial procedures is that of segmentation. This involves classifying a picture element (pixel) as either cell wall or lumen. The accuracy of tracheid measurements is dependent on how well the result of the segmentation procedure corresponds to the true distributions of cell wall or lumen pixels. In this paper a comparison of segmentation methods is given. The effect of segmentation method on measurements is investigated and the performance of each method is discussed.We demonstrate that automated segmentation methods remove observer bias and are thus capable of more reproducible results. The contrast for confocal microscope images is of such quality that one of the fastest and simplest automatic segmentation methods may be used.

scholarly journals Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods

2019 ◽  
Vol 30 (2) ◽  
pp. 1062-1074 ◽  
Author(s):  
A. de Sitter ◽  
◽  
M. Visser ◽  
I. Brouwer ◽  
K. S. Cover ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Prediction Algorithm ◽  
Facial Features ◽  
Study Group ◽  
Brain Volumes ◽  
Analysis Methods ◽  
Absolute Agreement ◽  
Multiple Sclerosis Patients ◽  
The Impact

Abstract Background Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants’ privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. Methods FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests. Results Automated analysis methods failed in 0–19% of cases in FFR-processed images versus 0–2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p < 0.001). Conclusions All three outcome measures were affected differently by FFR, including failure of analysis methods and both “random” variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants’ privacy. Key Points • Protecting participants’ privacy when sharing MRI data is important. • Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups. • Removing facial features degrades performance of image analysis methods.

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.

Interfacing of a TEM/STEM System to a Computer

1981 ◽  
Vol 39 ◽  
pp. 250-251
Author(s):  
P. Hagemann
Keyword(s):  
Image Analysis ◽  
Transmission Rate ◽  
Analytical Electron Microscopy ◽  
Signal Beam ◽  
Automated Image Analysis ◽  
Beam Deflection ◽  
External Control ◽  
Computer Input ◽  
Instrument Control ◽  
Data Acquisition And Processing

The use of computers in the analytical electron microscopy today shows three different trends (1) automated image analysis with dedicated computer systems, (2) instrument control by microprocessors and (3) data acquisition and processing e.g. X-ray or EEL Spectroscopy.While image analysis in the T.E.M. usually needs a television chain to get a sequential transmission suitable as computer input, the STEM system already has this necessary facility. For the EM400T-STEM system therefore an interface was developed, that allows external control of the beam deflection in TEM as well as the control of the STEM probe and video signal/beam brightness on the STEM screen.The interface sends and receives analogue signals so that the transmission rate is determined by the convertors in the actual computer periphery.

scholarly journals Detecting Apples in the Wild: Potential for Harvest Quantity Estimation

2021 ◽  
Vol 13 (14) ◽  
pp. 8054
Author(s):  
Artur Janowski ◽  
Rafał Kaźmierczak ◽  
Cezary Kowalczyk ◽  
Jakub Szulwic
Keyword(s):  
Image Analysis ◽  
Pilot Study ◽  
Production Management ◽  
Satellite System ◽  
Morphological Operations ◽  
Computational Performance ◽  
Analysis Methods ◽  
Manual Counting ◽  
In The Wild ◽  
Global Navigation Satellite

Knowing the exact number of fruits and trees helps farmers to make better decisions in their orchard production management. The current practice of crop estimation practice often involves manual counting of fruits (before harvesting), which is an extremely time-consuming and costly process. Additionally, this is not practicable for large orchards. Thanks to the changes that have taken place in recent years in the field of image analysis methods and computational performance, it is possible to create solutions for automatic fruit counting based on registered digital images. The pilot study aims to confirm the state of knowledge in the use of three methods (You Only Look Once—YOLO, Viola–Jones—a method based on the synergy of morphological operations of digital imagesand Hough transformation) of image recognition for apple detecting and counting. The study compared the results of three image analysis methods that can be used for counting apple fruits. They were validated, and their results allowed the recommendation of a method based on the YOLO algorithm for the proposed solution. It was based on the use of mass accessible devices (smartphones equipped with a camera with the required accuracy of image acquisition and accurate Global Navigation Satellite System (GNSS) positioning) for orchard owners to count growing apples. In our pilot study, three methods of counting apples were tested to create an automatic system for estimating apple yields in orchards. The test orchard is located at the University of Warmia and Mazury in Olsztyn. The tests were carried out on four trees located in different parts of the orchard. For the tests used, the dataset contained 1102 apple images and 3800 background images without fruits.

scholarly journals Author Correction: Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julian Bär ◽  
Mathilde Boumasmoud ◽  
Roger D. Kouyos ◽  
Annelies S. Zinkernagel ◽  
Clément Vulin
Keyword(s):  
Image Analysis ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Automated Image Analysis ◽  
Analysis Application

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