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Medical Image Blind Integrity Verification with Krawtchouk Moments
A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accompl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051270/ https://www.ncbi.nlm.nih.gov/pubmed/30057592 http://dx.doi.org/10.1155/2018/2572431 |
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author | Zhang, Xu Liu, Xilin Chen, Yang Shu, Huazhong |
author_facet | Zhang, Xu Liu, Xilin Chen, Yang Shu, Huazhong |
author_sort | Zhang, Xu |
collection | PubMed |
description | A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accomplished by classifying images into the original and modified categories. Experiments conducted on medical images issued from different modalities verified the validity of the proposed method and demonstrated that it can be used to detect and discriminate image modifications of different types with high accuracy. We also compared the performance of our scheme with a state-of-the-art solution suggested for medical images—solution that is based on histogram statistical properties of reorganized block-based Tchebichef moments. Conducted tests proved the better behavior of our image feature set. |
format | Online Article Text |
id | pubmed-6051270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-60512702018-07-29 Medical Image Blind Integrity Verification with Krawtchouk Moments Zhang, Xu Liu, Xilin Chen, Yang Shu, Huazhong Int J Biomed Imaging Research Article A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accomplished by classifying images into the original and modified categories. Experiments conducted on medical images issued from different modalities verified the validity of the proposed method and demonstrated that it can be used to detect and discriminate image modifications of different types with high accuracy. We also compared the performance of our scheme with a state-of-the-art solution suggested for medical images—solution that is based on histogram statistical properties of reorganized block-based Tchebichef moments. Conducted tests proved the better behavior of our image feature set. Hindawi 2018-07-02 /pmc/articles/PMC6051270/ /pubmed/30057592 http://dx.doi.org/10.1155/2018/2572431 Text en Copyright © 2018 Xu Zhang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Xu Liu, Xilin Chen, Yang Shu, Huazhong Medical Image Blind Integrity Verification with Krawtchouk Moments |
title | Medical Image Blind Integrity Verification with Krawtchouk Moments |
title_full | Medical Image Blind Integrity Verification with Krawtchouk Moments |
title_fullStr | Medical Image Blind Integrity Verification with Krawtchouk Moments |
title_full_unstemmed | Medical Image Blind Integrity Verification with Krawtchouk Moments |
title_short | Medical Image Blind Integrity Verification with Krawtchouk Moments |
title_sort | medical image blind integrity verification with krawtchouk moments |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051270/ https://www.ncbi.nlm.nih.gov/pubmed/30057592 http://dx.doi.org/10.1155/2018/2572431 |
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