Characterization of spatiotemporal stress distribution during food fracture by image texture analysis methods

Texture analysis plays an important role in image processing. In the field of texture analysis, the regular texture has been studied a lot, but the natural texture with complex backgrounds is less studied. This paper brings texture analysis into the study of rice paper's classification. First of all it shows the processing flow chart of rice paper classification. By comparing the different kinds of texture analysis methods it chooses the LAWS texture method and uncertainty texture spectrum method to achieve the rice paper classification. When it uses the two texture analysis methods separately, the classification accuracy of rice paper is lower, so it tries to combine the two texture analysis methods. The experimental results show that the classification result got with two combined texture analysis methods is better than that got with one single texture analysis method. The classification accuracy of rice paper has been distinctly improved after the combination of the two texture analysis methods.

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Computer assisted characterization of liver tissue using image texture analysis techniques on B-scan images

Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136) ◽

10.1109/iembs.1997.757766 ◽

2002 ◽

Cited By ~ 6

Author(s):

E. Kyriacou ◽

S. Pavlopoulos ◽

D. Koutsouris ◽

P. Zoumpoulis ◽

I. Theotokas

Keyword(s):

Texture Analysis ◽

Liver Tissue ◽

Image Texture ◽

Computer Assisted ◽

Image Texture Analysis ◽

Analysis Techniques ◽

B Scan Images

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Automatic characterization of fracture surfaces of AISI 304LN stainless steel using image texture analysis

Measurement ◽

10.1016/j.measurement.2012.01.026 ◽

2012 ◽

Vol 45 (5) ◽

pp. 1140-1150 ◽

Cited By ~ 24

Author(s):

Samik Dutta ◽

Arpan Das ◽

Kaustav Barat ◽

Himadri Roy

Keyword(s):

Stainless Steel ◽

Texture Analysis ◽

Image Texture ◽

Image Texture Analysis ◽

Fracture Surfaces ◽

304Ln Stainless Steel

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Characterization of bread breakdown during mastication by image texture analysis

Journal of Food Engineering ◽

10.1016/j.jfoodeng.2012.07.015 ◽

2012 ◽

Vol 113 (4) ◽

pp. 615-622 ◽

Cited By ~ 42

Author(s):

Carole Tournier ◽

Manon Grass ◽

Dhananjay Zope ◽

Christian Salles ◽

Dominique Bertrand

Keyword(s):

Texture Analysis ◽

Image Texture ◽

Image Texture Analysis

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CHARACTERIZATION OF HIPPOCAMPUS ON EPILEPTIC PATIENTS ON MRI USING TEXTURE ANALYSIS TECHNIQUES

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v9.i1.2021.2977 ◽

2021 ◽

Vol 9 (1) ◽

pp. 164-168

Author(s):

Tasneem Abdulrazig Mohamed Sayed ◽

Fatima Yousif Mohammed ◽

Maha Esmeal Ahmed

Keyword(s):

Texture Analysis ◽

Brain Mri ◽

Score Function ◽

Image Texture ◽

Mr Images ◽

Image Texture Analysis ◽

Analysis Techniques ◽

Classification Score ◽

Epileptic Patients

The aim of this study was to characterize the hippocampus in Sudanese epileptic patients in MR images using image texture analysis techniques in order to differentiate hippocampus between the normal and epileptic patient. There were two groups of the patients were examined by using Signal-GE 1.5Tesla MR Scanner which was used with patients with known epilepsy and normal T1 weighted brain. MRI finding patients, 101 and 105 patients respectively examined in period from December 2017- March 2018, where the variables of the study were MRI images entered to the IDL program as input for further analysis, using window 3*3 the images texture was extracted from hippocampus (head, body and tail) that include, mean, STD, variance, energy, and entropy then the comparison was made to differentiate between the normal and abnormal hippocampus. The extracted feature classified using linear discriminate analysis. The classification score function is used to classify the hippocampus classes was as flows: Epileptic= (.271×mean) + (.026×variance) + (7.475× Part) -32.134 Normal= (.240×mean) + (.052×variance) + (2.960× Part) -13.684 The study confirmed that it’s possible to differentiate between normal and epileptic hippocampus body, head, and tail in sagittal section texturally. The result showed that the classification result is best in the tail where higher classification accuracy will be achieved followed by body and then head.

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