Chain coding streamed images through crack run-length encoding

Author(s):  
D.G. Bailey
Keyword(s):  
1988 ◽  
Vol 41 (1) ◽  
pp. 114-128 ◽  
Author(s):  
Seong-Dae Kim ◽  
Jeong-Hwan Lee ◽  
Jae-Kyoon Kim

1992 ◽  
Vol 139 (2) ◽  
pp. 224 ◽  
Author(s):  
A.B. Johannessen ◽  
R. Prasad ◽  
N.B.J. Weyland ◽  
J.H. Bons

Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.


2009 ◽  
Vol 28 (9) ◽  
pp. 2270-2273
Author(s):  
Xiao-tong YE ◽  
Yun DENG

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