Color deconvolution method with DAB scatter correction for bright field image analysis

2018 ◽  
Author(s):  
Emilia Andersson ◽  
Yao Nie ◽  
Christian Roessler ◽  
Oliver Grimm
Keyword(s):  
Image Analysis ◽  
Scatter Correction ◽  
Bright Field Image ◽  
Deconvolution Method ◽  
Bright Field ◽  
Color Deconvolution

Arsenic segregation in polysilicon

1983 ◽  
Vol 41 ◽  
pp. 154-155 ◽  
Author(s):  
P.E. Batson ◽  
C.R.M. Grovenor ◽  
D.A. Smith ◽  
C. Wong
Keyword(s):  
Incident Beam ◽  
Silicon Wafers ◽  
Chemical Polishing ◽  
Bright Field Image ◽  
Beam Size ◽  
Stem Analysis ◽  
Bright Field ◽  
Twin Boundaries ◽  
X Ray ◽  
Line Scan

In this work As doped polysilicon was deposited onto (100) silicon wafers by APCVD at 660°C from a silane-arsine mixture, followed by a ten minute anneal at 1000°C, and in one case a further ten minute anneal at 700°C. Specimens for TEM and STEM analysis were prepared by chemical polishing. The microstructure, which is unchanged by the final 700°C anneal,is shown in Figure 1. It consists of numerous randomly oriented grains many of which contain twins.X-ray analysis was carried out in a VG HB5 STEM. As K α x-ray counts were collected from STEM scans across grain and twin boundaries, Figures 2-4. The incident beam size was about 1.5nm in diameter, and each of the 20 channels in the plots was sampled from a 1.6nm length of the approximately 30nm line scan across the boundary. The bright field image profile along the scanned line was monitored during the analysis to allow correlation between the image and the x-ray signal.

Lattice Imaging of Twin, Lath and α/Martensite Boundaries in Duplex Low Carbon Steels

1977 ◽  
Vol 35 ◽  
pp. 118-119
Author(s):  
J. Y. Koo ◽  
G. Thomas
Keyword(s):  
High Resolution Electron Microscopy ◽  
Ferrite Matrix ◽  
Carbon Steels ◽  
Bright Field Image ◽  
Low Carbon ◽  
Lattice Imaging ◽  
Bright Field ◽  
Lattice Image ◽  
New Information ◽  
Resolution Electron

High resolution electron microscopy has been shown to give new information on defects(1) and phase transformations in solids (2,3). In a continuing program of lattice fringe imaging of alloys, we have applied this technique to the martensitic transformation in steels in order to characterize the atomic environments near twin, lath and αmartensite boundaries. This paper describes current progress in this program.Figures A and B show lattice image and conventional bright field image of the same area of a duplex Fe/2Si/0.1C steel described elsewhere(4). The microstructure consists of internally twinned martensite (M) embedded in a ferrite matrix (F). Use of the 2-beam tilted illumination technique incorporating a twin reflection produced {110} fringes across the microtwins.

Correlation analysis of radiation damage

1978 ◽  
Vol 36 (1) ◽  
pp. 214-215
Author(s):  
R. Hegerl ◽  
A. Feltynowski ◽  
B. Grill
Keyword(s):  
Electron Microscopy ◽  
Independent Random Variables ◽  
Optical Parameters ◽  
Bright Field Image ◽  
Bright Field ◽  
Expectation Value ◽  
All Optical ◽  
Image Element ◽  
Stochastic Series ◽  
The Common

Till now correlation functions have been used in electron microscopy for two purposes: a) to find the common origin of two micrographs representing the same object, b) to check the optical parameters e. g. the focus. There is a third possibility of application, if all optical parameters are constant during a series of exposures. In this case all differences between the micrographs can only be caused by different noise distributions and by modifications of the object induced by radiation.Because of the electron noise, a discrete bright field image can be considered as a stochastic series Pm,where i denotes the number of the image and m (m = 1,.., M) the image element. Assuming a stable object, the expectation value of Pm would be Ηm for all images. The electron noise can be introduced by addition of stationary, mutual independent random variables nm with zero expectation and the variance. It is possible to treat the modifications of the object as a noise, too.

A Newly Designed 650kv Electron Microscope

1968 ◽  
Vol 26 ◽  
pp. 304-305
Author(s):  
K. Shiraishi ◽  
T. Katsuta ◽  
S. Ozasa ◽  
H. Todokoro
Keyword(s):  
Electron Microscope ◽  
High Voltage ◽  
Bright Field Image ◽  
Bright Field ◽  
Push Button ◽  
Condenser Lens ◽  
High Voltage Generator ◽  
Frequency Power ◽  
Better Than ◽  
Illuminating System

We have recently completed a newly designed 650KV electron microscope. An external view of this advanced instrument is shown in Figure 1. A symmetrical Cockcroft-Walton circuit has been adopted as the high voltage generator. The cathode is heated by high frequency power; a battery is not employed. The high voltage stability is better than 1 x 10-5/min.The sectional diagram of the column shown in Figure 2 is 420mm in diameter and 2750mm in height. The illuminating system consists of a double condenser lens and a magnetic alignment device. Dual deflector assemblies for dark and bright field images, selectable by push button, are built beneath the condenser lens. Two selectable stigmator power supplies are also provided for dark and bright field image operation.

scholarly journals Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (16) ◽  
pp. 3362 ◽  
Author(s):  
Shang Shang ◽  
Ling Long ◽  
Sijie Lin ◽  
Fengyu Cong
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Deep Convolutional Neural Network ◽  
Microscopic Image ◽  
Bright Field ◽  
Microscopic Images ◽  
Phenotype Classification ◽  
Microscopic Image Analysis

Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis.

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