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Published in: Journal of Digital Imaging 2/2019

01-04-2019

Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image

Authors: Oishila Bandyopadhyay, Arindam Biswas, Bhargab B. Bhattacharya

Published in: Journal of Imaging Informatics in Medicine | Issue 2/2019

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Abstract

Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-cost diagnostic tool for diagnosis and visualization of bone cancer. In this paper, a novel technique for the assessment of cancer stage and grade in long bones based on X-ray image analysis has been proposed. Cancer-affected bone images usually appear with a variation in bone texture in the affected region. A fusion of different methodologies is used for the purpose of our analysis. In the proposed approach, we extract certain features from bone X-ray images and use support vector machine (SVM) to discriminate healthy and cancerous bones. A technique based on digital geometry is deployed for localizing cancer-affected regions. Characterization of the present stage and grade of the disease and identification of the underlying bone-destruction pattern are performed using a decision tree classifier. Furthermore, the method leads to the development of a computer-aided diagnostic tool that can readily be used by paramedics and doctors. Experimental results on a number of test cases reveal satisfactory diagnostic inferences when compared with ground truth known from clinical findings.
Footnotes
1
Magnetic resonance imaging
 
2
Computed tomography
 
3
Single photon emission computed tomography
 
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Metadata
Title
Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image
Authors
Oishila Bandyopadhyay
Arindam Biswas
Bhargab B. Bhattacharya
Publication date
01-04-2019
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 2/2019
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-018-0145-0

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