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...

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Main Authors: Xu Zhang, Xilin Liu, Yang Chen, Huazhong Shu
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:International Journal of Biomedical Imaging
Online Access:http://dx.doi.org/10.1155/2018/2572431
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author Xu Zhang
Xilin Liu
Yang Chen
Huazhong Shu
author_facet Xu Zhang
Xilin Liu
Yang Chen
Huazhong Shu
author_sort Xu Zhang
collection DOAJ
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.
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institution Kabale University
issn 1687-4188
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language English
publishDate 2018-01-01
publisher Wiley
record_format Article
series International Journal of Biomedical Imaging
spelling doaj-art-43ea6efe6b8143479b9782bbed1a02852025-08-20T03:36:06ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962018-01-01201810.1155/2018/25724312572431Medical Image Blind Integrity Verification with Krawtchouk MomentsXu Zhang0Xilin Liu1Yang Chen2Huazhong Shu3Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, ChinaLaboratory of Image Science and Technology, Southeast University, Nanjing 210096, ChinaLaboratory of Image Science and Technology, Southeast University, Nanjing 210096, ChinaLaboratory of Image Science and Technology, Southeast University, Nanjing 210096, ChinaA 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.http://dx.doi.org/10.1155/2018/2572431
spellingShingle Xu Zhang
Xilin Liu
Yang Chen
Huazhong Shu
Medical Image Blind Integrity Verification with Krawtchouk Moments
International Journal of Biomedical Imaging
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
url http://dx.doi.org/10.1155/2018/2572431
work_keys_str_mv AT xuzhang medicalimageblindintegrityverificationwithkrawtchoukmoments
AT xilinliu medicalimageblindintegrityverificationwithkrawtchoukmoments
AT yangchen medicalimageblindintegrityverificationwithkrawtchoukmoments
AT huazhongshu medicalimageblindintegrityverificationwithkrawtchoukmoments