Extraction of Texture Features from X-Ray Images: Case of Osteoarthritis Detection

被引：7

作者：

Akter, Mukti ^{[1
]}

Jakaite, Livija ^{[1
]}

机构：

[1] Univ Bedfordshire, Sch Comp Sci & Technol, Luton, Beds, England

来源：

THIRD INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY | 2019年 / 797卷

关键词：

Texture features; Deep learning; X-ray imaging; NEURAL-NETWORKS; PREDICTION; CLASSIFICATION; CONFIDENCE;

D O I：

10.1007/978-981-13-1165-9_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Texture features quantitatively represent patterns of interest in image analysis and interpretation. Texture features can vary so largely that the analysis leads to interpretation errors and undesirable consequences. In such cases, finding of informative features becomes problematic. In medical imaging, the texture features were found useful for representing variations in patterns of pixel intensity, which were correlated with pathological changes. In this paper, we describe a new approach to extracting the texture features which are represented on the basis of Zernike orthogonal polynomials. We report the preliminary results which were obtained for a case of osteoarthritis detection in X-ray images using a deep learning paradigm known as group method of data handling.

引用

页码：143 / 150

页数：8

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