Computer-Assisted Image Processing Techniques for Quantitative Analysis of Cell Migrations on Collagen-Coated Glass

2005 ◽  
pp. 503-506
Author(s):  
B.J. Park ◽  
Seok Cheol Kim ◽  
D.H. Lee ◽  
Hyun Joo Son ◽  
K.C. Nam ◽  
...  
Keyword(s):  
Image Processing ◽  
Quantitative Analysis ◽  
Computer Assisted ◽  
Image Processing Techniques ◽  
Coated Glass ◽  
Computer Assisted Image Processing ◽  
Computer Assisted Image ◽  
Processing Techniques

scholarly journals Quantitative Analysis of Mineral Composition of Iron Ore Sinter Based on Comprehensive Image Processing Techniques

2014 ◽  
Vol 54 (6) ◽  
pp. 1222-1227 ◽  
Author(s):  
Hong-wei Guo ◽  
Bu-xin Su ◽  
Zhen-long Bai ◽  
Jian-liang Zhang ◽  
Xin-yu Li ◽  
...  
Keyword(s):  
Image Processing ◽  
Quantitative Analysis ◽  
Mineral Composition ◽  
Iron Ore ◽  
Image Processing Techniques ◽  
Iron Ore Sinter ◽  
Processing Techniques

scholarly journals Computer-assisted image processing to detect spores from the fungus Pandora neoaphidis

MethodsX ◽  
2016 ◽  
Vol 3 ◽  
pp. 231-241 ◽  
Author(s):  
Reinert Korsnes ◽  
Karin Westrum ◽  
Erling Fløistad ◽  
Ingeborg Klingen
Keyword(s):  
Image Processing ◽  
Computer Assisted ◽  
Computer Assisted Image Processing ◽  
Computer Assisted Image ◽  
Pandora Neoaphidis

Using the “Blur Tolerance” Technique to Predict and Optimize the Legibility Distance of Symbol Highway Signs

1994 ◽  
Vol 38 (14) ◽  
pp. 912-915 ◽  
Author(s):  
Frank Schieber
Keyword(s):  
Image Processing ◽  
Spatial Frequency ◽  
Digital Image Processing ◽  
High Spatial Frequency ◽  
Recognition Threshold ◽  
Computer Assisted ◽  
Image Processing Techniques ◽  
Threshold Data ◽  
Processing Techniques ◽  
Highway Signs

This study investigates the hypothesis that variations in symbol sign legibility distance can be accounted for on the basis of a sign's dependence upon high spatial frequency contours to convey critical information. Using digital image processing techniques, highway signs were blurred to remove all high spatial frequency information. A blur recognition threshold was established for each experimental sign by sequentially “deblurring” it until the observer could report the critical details defining its recognition criteria. Correlational analyses were then conducted to determine if legibility distance (collected in a previous study) could be predicted from the blur recognition threshold data. A significant correlation was observed between blur recognition threshold and sign legibility distance (r = −0.734, N=12, p < 0.001). That is, symbol signs with high levels of “blur tolerance” could be recognized at significantly greater viewing distances. These results support the application of new computer-assisted “recursive-blur” design techniques to optimize the effectiveness of symbol highway signs and related visual stimuli (see Schieber, Kline and Dewar, 1994).

An objective method to measure cell survival by computer-assisted image processing of numeric images of Petri dishes

2003 ◽  
Vol 48 (11) ◽  
pp. 1551-1563 ◽  
Author(s):  
Marie-Claude Biston ◽  
St phanie Corde ◽  
Emmanuel Camus ◽  
Ramon Marti-Battle ◽  
Fran ois Est ve ◽  
...  
Keyword(s):  
Image Processing ◽  
Cell Survival ◽  
Computer Assisted ◽  
Objective Method ◽  
Computer Assisted Image Processing ◽  
Computer Assisted Image ◽  
Petri Dishes

scholarly journals Advanced Region Detection Technique to Enhance Accurate Abnormality Isolation of Cancer in Digital Images

2019 ◽  
Vol 8 (S1) ◽  
pp. 28-32
Author(s):  
N. M. Mallika ◽  
S. Janarthanam ◽  
A. Aruljoth
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Contrast Enhancement ◽  
Cancer Detection ◽  
Computer Assisted ◽  
Image Processing Techniques ◽  
Region Detection ◽  
Considerable Research ◽  
Computer Aided ◽  
Processing Techniques

In recent years, extensive research is carried out in computer assisted interpretation carried out for cancer classification. Computer aided Interpretations are involves with pre-processing, contrast enhancement, segmentation, appropriate feature extraction and classification. Though considerable research is carried out in developing contrast enhancement and image segmentation techniques, cancer regions could not be isolated and extracted efficiently. Hence this work focuses on developing efficient image segmentation techniques for isolating the cancer region and also identifying suitable descriptors for describing the cancer region. Hence this work focuses to introduce a simple and easy approach for detection of cancerous tissues in mammals. Detection phase is followed by segmentation of the region in an image. Our approach uses simple image processing techniques such as averaging and thresholding along with a Max-Mean and Least-Variance technique for cancer detection. Experimental results demonstrate the effectiveness of our approach.

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