The state and quantitative characterization of congested capillaries in the brain in conditions of acute and chronic venous congestion

2000 ◽  
Vol 30 (1) ◽  
pp. 115-117
Author(s):  
S. V. Chemezov
Keyword(s):  
The State ◽  
Venous Congestion ◽  
Quantitative Characterization ◽  
The Brain

Quantitative Characterization of Cure. II. Use of Modulus as a Measure of the State of Cure of Pure-Gum Natural-Rubber Vulcanizates

1952 ◽  
Vol 25 (3) ◽  
pp. 439-446 ◽  
Author(s):  
W. P. Fletcher ◽  
Geoffrey Gee ◽  
S. H. Morrell
Keyword(s):  
Natural Rubber ◽  
The State ◽  
Quantitative Characterization ◽  
Natural Rubber Vulcanizates ◽  
Practical Disadvantage ◽  
Practical Measure

Abstract The purpose of this investigation was to develop methods of measuring state of cure which involved only measurements normally available or very readily made. Swelling measurements, although suitable in theory, suffer the practical disadvantage of being time-consuming, but it is shown that they can be replaced very satisfactorily by modulus at 100 per cent elongation. This is a good practical measure of state of cure for most purposes, and simple corrections are developed which extend its range of usefulness. An easily constructed machine is described for the accurate measure of modulus at small elongations.

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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