A CGA-MRF Hybrid Method for Iris Texture Analysis and Modeling

2014 ◽  
Author(s):  
Ma Lin ◽  
He Ying ◽  
Li Haifeng ◽  
Li Naimin ◽  
David Zhang
Keyword(s):  
Texture Analysis ◽  
Hybrid Method

Treatments of Plankton and Escherichia coli Cells using Hybrid Method with Water Cavitation and Discharge Plasma

2015 ◽  
Vol 135 (6) ◽  
pp. 357-365
Author(s):  
Satoshi Ihara ◽  
Hironori Itoh ◽  
Noriki Kobayashi ◽  
Yuko Inoue ◽  
Hiroaki Terato ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Hybrid Method ◽  
Discharge Plasma ◽  
I Cells

A Hybrid Method for Distribution Substation Reliability Evaluation

2017 ◽  
Vol 12 (2) ◽  
pp. 142
Author(s):  
Hemakumar Reddy Galiveeti ◽  
Arup Kumar Goswami ◽  
Nalin B. Dev Choudhury
Keyword(s):  
Hybrid Method ◽  
Reliability Evaluation

Statistical Nuclear Texture Analysis in Cancer Research: A Review of Methods and Applications

2008 ◽  
Vol 14 (2-3) ◽  
pp. 89-164 ◽  
Author(s):  
Birgitte Nielsen ◽  
Fritz Albregtsen ◽  
Havard E. Danielsen
Keyword(s):  
Cancer Research ◽  
Texture Analysis ◽  
Nuclear Texture

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

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.

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