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International Journal of Biomedical Imaging
Volume 2018, Article ID 2572431, 11 pages
Research Article

Medical Image Blind Integrity Verification with Krawtchouk Moments

1Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
2College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China
3Centre de Recherche en Information Biomédicale Sino-Français, Nanjing 210096, China
4International Joint Research Laboratory of Information Display and Visualization, Southeast University, Ministry of Education, Nanjing 210096, China

Correspondence should be addressed to Huazhong Shu; nc.ude.ues@tsil.uhs

Received 15 August 2017; Revised 19 November 2017; Accepted 31 May 2018; Published 2 July 2018

Academic Editor: Lizhi Sun

Copyright © 2018 Xu Zhang et al. 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.


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.