Digital Image Texture Analysis Using Gray Level and Energy Cooccurrence

1987 ◽  
Author(s):  
Zhang Dapeng ◽  
Li Zhongrong
Keyword(s):  
Texture Analysis ◽  
Digital Image ◽  
Image Texture ◽  
Gray Level ◽  
Image Texture Analysis

scholarly journals Selection of the optimum features to identify tooth decay in the panoramic images based on image texture analysis

2020 ◽  
Vol 27 (1) ◽  
pp. 130-139
Author(s):  
Alaa Khaled Zakaria ◽  
Yasser Khadra ◽  
Eid Al-Abboud
Keyword(s):  
Texture Analysis ◽  
Digital Image ◽  
Technological Development ◽  
Noise Removal ◽  
Extraction Process ◽  
Image Texture ◽  
Tooth Decay ◽  
Gray Level ◽  
Radiographic Images ◽  
Image Texture Analysis

The process of identifying pathological patterns in dental radiographic images (panorama images) is one of the most important stages of diagnosing diseases for dentists, and in light of the tremendous technological development, especially in the field of machine learning and pattern recognition, the Digital Image processing department has the most important role in the field of image fragmentation Extract the necessary features in order to identify pathological patterns and thus easily extract the pathological features of the input images. In this research, a methodology has been proposed to extract the features related to tooth decay from the digital Panorama radiographs obtained from the VaTech 400 device using image texture analysis based on the gray level co-occurrence matrix (GLCM) algorithm where the digital image was first entered into the computer and then converted to the Gray level, processed and noise removal facilities for the extraction process and then the statistical features of the GLCM matrix were extracted and then the choice of optimum features that lead to improved decay detection. The obtained results are shown increasing of accuracy of the results and improve the diagnosis process.

scholarly journals The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 195
Author(s):  
Małgorzata Domino ◽  
Marta Borowska ◽  
Anna Trojakowska ◽  
Natalia Kozłowska ◽  
Łukasz Zdrojkowski ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Texture Features ◽  
Image Texture ◽  
Gray Level ◽  
Image Texture Analysis ◽  
Thermal Images ◽  
Thoracolumbar Region ◽  
Thermal Features ◽  
Occurrence Matrix ◽  
The Impact

Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

scholarly journals Image Texture Analysis and Edge Detection Algorithm Based on Anisotropic Diffusion Equation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaoqin Li
Keyword(s):  
Texture Analysis ◽  
Level Set ◽  
Anisotropic Diffusion ◽  
Equivalent Form ◽  
Diffusion Equations ◽  
Image Texture ◽  
Gray Level ◽  
Image Texture Analysis ◽  
Anisotropic Diffusion Equation ◽  
Selection Of

This paper uses partial differential equation image processing techniques to establish image texture analysis models based on nonlinear anisotropic diffusion equations for image denoising, image segmentation, and image decomposition. This paper proposes a class of denoising models based on the hybrid anisotropic diffusion equation from the characteristics of different noise types. The model exhibits anisotropic diffusion near the image boundary, which can protect the boundary well, and isotropic diffusion inside the image; so, it can remove the noise effectively. We use the immovable point theory to prove the uniqueness of the model solution and further discuss other properties such as asymptotics of the solution. We propose a class of image texture analysis algorithms based on anisotropic diffusion equations and discrete gray level sets. First, a class of nonconvex generalized functions is proposed to remove the noise from the original image to obtain a smooth image while sharpening the edges. Then, an energy generalization function based on the gray level set is proposed, and the existence of the global minimum of this energy generalization function is discussed. Finally, an equivalent form of this energy generalization is given in the discrete case, and an image texture analysis algorithm is designed based on the equivalent form. The algorithm is improved by initial position optimization, dynamic adjustment of parameters, and adaptive selection of thresholds so that the ants can search along the real edges. Experiments show that the improved algorithm for image edge detection can obtain more complete edges and better detection results. The energy generalization function is calculated directly on the discrete gray level set instead of solving the corresponding partial differential equation, which can avoid the selection of the initial level set and the reinitialization of the level set, thus greatly improving the segmentation efficiency. The new algorithm has a high improvement in segmentation efficiency and can efficiently handle large size complex images.

scholarly journals Radiomics and Digital Image Texture Analysis in Oncology (Review)

2021 ◽  
Vol 13 (2) ◽  
pp. 97
Author(s):  
A.A. Litvin ◽  
D.A. Burkin ◽  
A.A. Kropinov ◽  
F.N. Paramzin
Keyword(s):  
Texture Analysis ◽  
Digital Image ◽  
Image Texture ◽  
Image Texture Analysis ◽  
Oncology Review

Image Texture Analysis based on Color Level Methods using Neural Network

2020 ELEKTRO ◽  
2020 ◽  
Author(s):  
Miroslav Benco ◽  
Patrik Kamencay ◽  
Robert Hudec ◽  
Martina Radilova ◽  
Peter Sykora
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis

Characterization of micrographs and fractographs of Cu-strengthened HSLA steel using image texture analysis

Measurement ◽  
2014 ◽  
Vol 47 ◽  
pp. 130-144 ◽  
Author(s):  
Samik Dutta ◽  
Kaustav Barat ◽  
Arpan Das ◽  
Swapan Kumar Das ◽  
A.K. Shukla ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Hsla Steel ◽  
Image Texture ◽  
Image Texture Analysis
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