Fractal model for digital image texture analysis

1993 ◽  
Author(s):  
Michael G. Petrolekas ◽  
Sunanda Mitra
Keyword(s):  
Texture Analysis ◽  
Digital Image ◽  
Fractal Model ◽  
Image Texture ◽  
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 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

scholarly journals NIMG-21. IMPACT OF GENETIC ALTERATIONS ON TUMOR LOCATIONS AND LESION HETEROGENEITY FOR WHO GRADE 2 AND 3 GLIOMAS: A VOXEL-BASED LESION MAPPING AND IMAGE TEXTURE ANALYSIS OF 201 GLIOMAS

2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi128-vi128
Author(s):  
Manabu Kinoshita ◽  
Hideyuki Arita ◽  
Toshiki Yoshimine ◽  
Masamichi Takahashi ◽  
Yoshitaka Narita ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Genetic Alterations ◽  
Image Texture ◽  
Who Grade ◽  
Image Texture Analysis ◽  
Lesion Mapping

An efficient method for image texture analysis

2004 ◽  
Author(s):  
Umasankar Kandaswamy ◽  
Donald A. Adjeroh ◽  
M. C. Lee
Keyword(s):  
Texture Analysis ◽  
Efficient Method ◽  
Image Texture ◽  
Image Texture Analysis
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