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Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentat...

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Autores principales: Hržić, Franko, Štajduhar, Ivan, Tschauner, Sebastian, Sorantin, Erich, Lerga, Jonatan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514822/
https://www.ncbi.nlm.nih.gov/pubmed/33267052
http://dx.doi.org/10.3390/e21040338
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author Hržić, Franko
Štajduhar, Ivan
Tschauner, Sebastian
Sorantin, Erich
Lerga, Jonatan
author_facet Hržić, Franko
Štajduhar, Ivan
Tschauner, Sebastian
Sorantin, Erich
Lerga, Jonatan
author_sort Hržić, Franko
collection PubMed
description The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentation and the detection of regions of interest. The proposed local Shannon entropy was calculated for each image pixel using a sliding 2D window. An initial image segmentation was performed on the entropy representation of the original image. Next, a graph theory-based technique was implemented for the purpose of removing false bone contours and improving the edge detection of long bones. Finally, the paper introduces a classification and localisation procedure for fracture detection by tracking the difference between the extracted contour and the estimation of an ideal healthy one. The proposed hybrid method excels at detecting small fractures (which are hard to detect visually by a radiologist) in the ulna and radius bones—common injuries in children. Therefore, it is imperative that a radiologist inspecting the X-ray image receives a warning from the computerised X-ray analysis system, in order to prevent false-negative diagnoses. The proposed method was applied to a data-set containing 860 X-ray images of child radius and ulna bones (642 fracture-free images and 218 images containing fractures). The obtained results showed the efficiency and robustness of the proposed approach, in terms of segmentation quality and classification accuracy and precision (up to [Formula: see text] and [Formula: see text] , respectively).
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spelling pubmed-75148222020-11-09 Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection Hržić, Franko Štajduhar, Ivan Tschauner, Sebastian Sorantin, Erich Lerga, Jonatan Entropy (Basel) Article The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentation and the detection of regions of interest. The proposed local Shannon entropy was calculated for each image pixel using a sliding 2D window. An initial image segmentation was performed on the entropy representation of the original image. Next, a graph theory-based technique was implemented for the purpose of removing false bone contours and improving the edge detection of long bones. Finally, the paper introduces a classification and localisation procedure for fracture detection by tracking the difference between the extracted contour and the estimation of an ideal healthy one. The proposed hybrid method excels at detecting small fractures (which are hard to detect visually by a radiologist) in the ulna and radius bones—common injuries in children. Therefore, it is imperative that a radiologist inspecting the X-ray image receives a warning from the computerised X-ray analysis system, in order to prevent false-negative diagnoses. The proposed method was applied to a data-set containing 860 X-ray images of child radius and ulna bones (642 fracture-free images and 218 images containing fractures). The obtained results showed the efficiency and robustness of the proposed approach, in terms of segmentation quality and classification accuracy and precision (up to [Formula: see text] and [Formula: see text] , respectively). MDPI 2019-03-28 /pmc/articles/PMC7514822/ /pubmed/33267052 http://dx.doi.org/10.3390/e21040338 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hržić, Franko
Štajduhar, Ivan
Tschauner, Sebastian
Sorantin, Erich
Lerga, Jonatan
Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection
title Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection
title_full Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection
title_fullStr Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection
title_full_unstemmed Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection
title_short Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection
title_sort local-entropy based approach for x-ray image segmentation and fracture detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514822/
https://www.ncbi.nlm.nih.gov/pubmed/33267052
http://dx.doi.org/10.3390/e21040338
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AT sorantinerich localentropybasedapproachforxrayimagesegmentationandfracturedetection
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