A Linkage Chewing Machine for Food Texture Analysis

2008 ◽  
Author(s):  
C. Sun ◽  
J.E. Bronlund ◽  
L. Huang ◽  
M. P. Morgenstern ◽  
W.L. Xu
Keyword(s):  
Texture Analysis ◽  
Food Texture

Simulation and Analysis of a Masticatory Robot Based on Biological Structure

2011 ◽  
Vol 345 ◽  
pp. 417-422
Author(s):  
Ming Cong ◽  
Tong Zhan Liu ◽  
Zhan Bo Chang ◽  
Wei Liang Xu
Keyword(s):  
Temporomandibular Joint ◽  
Texture Analysis ◽  
Speech Therapy ◽  
Biological Structure ◽  
Food Texture ◽  
Masticatory System ◽  
Chewing Behaviour

To meet the demand of the food texture analysis, dental training, speech therapy and temporomandibular joint (TMJ) research, a device is required to enable the reproduction of human chewing behaviour accurately. Based on the biological structure of masticatory system, this paper presented a robotic model for human chewing system in Solidworks, and analyzed the kinematics character for the masticatory robot via ADAMS. Results have showed that the robotic model is proper to reproduce the human chewing behaviour.

scholarly journals Cooking Methods for a Soft Diet Using Chicken Based on Food Texture Analysis

2017 ◽  
Vol 63 (4) ◽  
pp. 256-262
Author(s):  
Emi WATANABE ◽  
Masami MAENO ◽  
Jun KAYASHITA ◽  
Ken-ichi MIYAMOTO ◽  
Miho KOGIRIMA
Keyword(s):  
Texture Analysis ◽  
Cooking Methods ◽  
Soft Diet ◽  
Food Texture

A linkage chewing machine for food texture analysis

2010 ◽  
Vol 8 (1/2/3/4) ◽  
pp. 303 ◽  
Author(s):  
Cheng Sun ◽  
John Bronlund ◽  
W.L. Xu ◽  
Loulin Huang ◽  
Marco P Morgenstern
Keyword(s):  
Texture Analysis ◽  
Food Texture

Food Texture Analysis in the 21st Century

2011 ◽  
Vol 59 (5) ◽  
pp. 1477-1480 ◽  
Author(s):  
Michael H. Tunick
Keyword(s):  
Texture Analysis ◽  
21St Century ◽  
Food Texture

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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