IBIS integrated biological imaging system: electron micrograph image-processing software running on Unix workstations

1992 ◽  
Vol 8 (6) ◽  
pp. 583-586
Author(s):  
Mohamed J. Flifla ◽  
Mireille Garreau ◽  
Jean-Paul Rolland ◽  
Jean-Louis Coatrieux ◽  
Daniel Thomas
Keyword(s):  
Image Processing ◽  
Imaging System ◽  
Biological Imaging ◽  
Electron Micrograph ◽  
Processing Software ◽  
Image Processing Software

Research on the Multi-Spectrum Imaging System Based on Vision Technology

2012 ◽  
Vol 443-444 ◽  
pp. 488-494
Author(s):  
Xuan Hong Jin ◽  
Zheng Yang Zhou ◽  
Ran Xu
Keyword(s):  
Image Processing ◽  
Virtual Instrument ◽  
Imaging System ◽  
Color Image ◽  
Processing System ◽  
New Type ◽  
Spectrum Imaging ◽  
Processing Software ◽  
Image Processing Software ◽  
Labview Platform

This paper introduces an acquiring and processing system of a new type of optical spectrometer based on vision technology. It mainly introduces the hardware structure to acquire the spectrums dispersed by the spectrometer, and the multi-spectrum image processing software as well. Of the different spectrum wavelengths ranges from 400nm to 740nm, the system can create both the color image and the 68 channels gray scale image. Virtual instruments technology is introduced into this system and it makes programming easier and faster by combining virtual instrument and vision technology. The programming of the image processing software uses LabVIEW platform.

scholarly journals Deblurring Images

1998 ◽  
Vol 6 (2) ◽  
pp. 8-9
Author(s):  
Everett R. Ramer
Keyword(s):  
Image Processing ◽  
Imaging System ◽  
List Type ◽  
Type Number ◽  
Trial And Error ◽  
Single Image ◽  
Depth Of Focus ◽  
Processing Software ◽  
Image Processing Software ◽  
Simple Combination

If you are an optical microscopist, chances are you have sometimes wished for a way to increase the depth of focus of your images. In this article I describe a method that does this using a simple combination of functions built into most image processing software - so it will not cost you very much to try, The method, however, is not universal and some trial-and-error will be required to get it to work in different applications. Finally, this method is not limited to microscopy and will work with any imaging system.The basic steps for creating an image with a large depth of focus are straight forward:1.Take a series of images at different focal planes2.Remove the blurred content from each image3.Combine the debiurred images into a single image

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

scholarly journals Demineralization Depth Using QLF and a Novel Image Processing Software

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Jun Wu ◽  
Zachary R. Donly ◽  
Kevin J. Donly ◽  
Steven Hackmyer
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Polarized Light ◽  
Region Of Interest ◽  
Linear Regression Method ◽  
Percent Loss ◽  
Significant Linear Correlation ◽  
Processing Software ◽  
Image Processing Software ◽  
Imagej Software

Quantitative Light-Induced fluorescence (QLF) has been widely used to detect tooth demineralization indicated by fluorescence loss with respect to surrounding sound enamel. The correlation between fluorescence loss and demineralization depth is not fully understood. The purpose of this project was to study this correlation to estimate demineralization depth. Extracted teeth were collected. Artificial caries-like lesions were created and imaged with QLF. Novel image processing software was developed to measure the largest percent of fluorescence loss in the region of interest. All teeth were then sectioned and imaged by polarized light microscopy. The largest depth of demineralization was measured by NIH ImageJ software. The statistical linear regression method was applied to analyze these data. The linear regression model wasY=0.32X+0.17, whereXwas the percent loss of fluorescence andYwas the depth of demineralization. The correlation coefficient was 0.9696. The two-tailed t-test for coefficient was 7.93, indicating theP-value=.0014. TheFtest for the entire model was 62.86, which shows theP-value=.0013. The results indicated statistically significant linear correlation between the percent loss of fluorescence and depth of the enamel demineralization.

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