Work place: Department of Computer Science, Loughborough University, UK
E-mail: B.Li@lboro.ac.uk
Website:
Research Interests: Pattern Recognition, Information Systems, Information Storage Systems, Multimedia Information System, Information Theory
Biography
Dr. Baihua Li received a PhD degree in Computer Science from Aberystwyth University. Currently she is a Senior Lecturer in the Dept. of Computer Science at Loughborough University. She has a solid research track record in computer vision, machine learning, pattern recognition and image processing. More than 70 papers have been published in high impact journals and conferences, including the most prestigious scientific journals in AI: IEEE Trans. Industrial Informatics, IEEE Trans. System, Man, Cybernetics, IEEE Trans. Biomed Eng., Pattern Recognition, Information Sciences, and IEEE Journal of Biomedical and Health Informatics. She contributed to a number of projects as PI/Co-I, including projects funded by EPSRC, Innovate UK, NHS, local and international industry and research institutions.
By Ranvir Singh Bhogal Baihua Li Alastair Gale Yan Chen
DOI: https://doi.org/10.5815/ijitcs.2018.08.01, Pub. Date: 8 Aug. 2018
Under the Digital Image and Communication in Medicine (DICOM) standard, the Advanced Encryption Standard (AES) is used to encrypt medical image pixel data. This highly sensitive data needs to be transmitted securely over networks to prevent data modification. Therefore, there is ongoing research into how well encryption algorithms perform on medical images and whether they can be improved. In this paper, we have developed an algorithm using a chaotic map combined with AES and tested it against AES in its standard form. This comparison allowed us to analyse how the chaotic map affected the encryption quality. The developed algorithm, CAT-AES, iterates through Arnold’s cat map before encryption a certain number of times whereas, the standard AES encryption does not. Both algorithms were tested on two sets of 16-bit DICOM images: 20 brain MRI and 26 breast cancer MRI scans, using correlation coefficient and histogram uniformity for evaluation. The results showed improvements in the encryption quality. When encrypting the images with CAT-AES, the histograms were more uniform, and the absolute correlation coefficient was closer to zero for the majority of images tested on.
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