image analyzers
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2018 ◽  
Vol 40 (1) ◽  
Author(s):  
Takahiro Kyoya ◽  
Rika Iwamoto ◽  
Yuko Shimanura ◽  
Megumi Terada ◽  
Shuichi Masuda
Keyword(s):  

2008 ◽  
Vol 51 (1) ◽  
pp. 107-112 ◽  
Author(s):  
V. G. Panteleev ◽  
V. A. Slaev ◽  
A. G. Chunovkina

2007 ◽  
Vol 15 (1) ◽  
pp. 6-13
Author(s):  
Fred Schamber ◽  
Kai van Beek

At the August M&M-2006 meeting in Chicago, we were standing next to our poster titled A Different Kind of Microscopy: Analyzing Features with an Automated Electron Beam when an acquaintance with long experience in electron microscopy wandered by. After a glance at the poster title, he challenged: “What's different about that?” Upon hearing our summary he asked (with what we took to be an encouraging tone of voice): “Are you going to publish this?” We had enough similar reactions from others to make that seem a good idea, and this is the result.This discussion should begin by noting that the operative word in the title is “different,” not “new.” In point of fact, the foundations for the technique were laid in the 1970s when some workers began putting scanning electron microscopes and microprobes under software control by interfacing them to the “minicomputers” that powered the computerized x-ray analyzer units then entering the market. Even prior to this, there were a few “hard-wired” image analyzers that mechanized the process of extracting information from microscope images. Thus, automated analysis of features via an electron beam instrument is hardly a new concept.


2001 ◽  
Author(s):  
I. Y. Barsky ◽  
Alexandr P. Grammatin ◽  
A. V. Ivanov ◽  
E. I. Kreindline ◽  
E. V. Kotova ◽  
...  

1988 ◽  
Vol 11 (3) ◽  
pp. 133-136 ◽  
Author(s):  
Daniel Chappard ◽  
Muriel Petitjean ◽  
Sabine Palle ◽  
Laurence Vico ◽  
Christian Alexandre

1986 ◽  
Vol 6 (4) ◽  
pp. 499-504 ◽  
Author(s):  
James L. Lear

The relationships between tissue tracer concentrations, length of time of tissue contact with film, and the darkness of resulting autoradiographic images were explored. Operational equations were then developed relating ranges of film darkening to the ranges of tracer concentration contained within the sections. These equations were solved and used to define ranges of optical density that are optimal for precise determination of tracer concentration using digital image analyzers. The solutions indicated that optimal optical densities are a function of the range of tracer concentrations in the sections. For autoradiograms of typical cerebral blood flow and metabolism tracers, exposure should be controlled to produce images that are significantly less dark than what is generally considered pleasing to the eye.


Bone ◽  
1985 ◽  
Vol 6 (5) ◽  
pp. 413-413
Author(s):  
M.E. Arlot ◽  
P.M. Chavassieux ◽  
P.J. Meunier

1981 ◽  
Vol 59 (8) ◽  
pp. 1611-1614 ◽  
Author(s):  
W. Gary Sprules ◽  
L. Blair Holtby ◽  
Gerald Griggs

As an inexpensive, more versatile alternative to the Coulter counter and image analyzers for the acquisition of size data in biological research, a semiautomated measuring device with associated data storage and retrieval capabilities has been developed. It can be used for measurement of such diverse characters as zooplankton body size and helmet lengths, phytoplankton size, fish scale measurements, fish egg diameters, and general taxonomic features such as body proportions and inter-setule distances. Any organism, or part thereof, which can be scaled to the device's range of approximately 1–7 cm can be quickly and accurately counted and (or) measured.


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