Extraction of texture features from x-ray images: case of osteoarthritis detection

Mukti Akter; Livija Jakaite

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

Extraction of texture features from x-ray images: case of osteoarthritis detection

Mukti Akter
, Livija Jakaite

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Third International Congress on Information and Communication Technology
Publisher	Springer
Pages	143-150
Volume	797
DOIs	https://doi.org/10.1007/978-981-13-1165-9_13
Publication status	Published - 29 Sept 2018
Event	Third International Congress on Information and Communication Technology ICICT 2018 - London Duration: 27 Feb 2018 → 28 Feb 2018

Conference

Conference	Third International Congress on Information and Communication Technology ICICT 2018
City	London
Period	27/02/18 → 28/02/18
Other	Third International Congress on Information and Communication Technology ICICT 2018 (27/02/2018-28/02/2018, London)

Keywords

feature extraction

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

https://link.springer.com/chapter/10.1007/978-981-13-1165-9_13

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title = "Extraction of texture features from x-ray images: case of osteoarthritis detection",

abstract = "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.",

keywords = "feature extraction",

author = "Mukti Akter and Livija Jakaite",

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AU - Akter, Mukti

AU - Jakaite, Livija

PY - 2018/9/29

Y1 - 2018/9/29

N2 - 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.

AB - 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.

KW - feature extraction

U2 - 10.1007/978-981-13-1165-9_13

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

M3 - Conference contribution

VL - 797

SP - 143

EP - 150

BT - Third International Congress on Information and Communication Technology

PB - Springer

T2 - Third International Congress on Information and Communication Technology ICICT 2018

Y2 - 27 February 2018 through 28 February 2018

ER -

Extraction of texture features from x-ray images: case of osteoarthritis detection

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Keywords

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