Alastair Gale

Work place: Department of Computer Science, Loughborough University, UK

E-mail: A.G.Gale@lboro.ac.uk

Website:

Research Interests: Medical Informatics, Image Compression, Image Manipulation, Security Services, Medical Image Computing

Biography

Professor Alastair Gale received a PhD from Durham University.  He is Emeritus Professor of Applied Vision Sciences at Loughborough University and formerly led the Applied Vision Research Centre at Loughborough which he established.  He is a Chartered Psychologist and Fellow of the British Psychological Society, a Fellow of the Institute of Ergonomics and Human Factors and an Honorary Fellow of the Royal College of Radiologists.  His research spans medical image interpretation, homeland security, assistive technology, driving and visual search in applied situations.  He has held many research grants as PI/Co-PI from the EU, NIHR, EPSRC, DTI, MoD, NPSA, NHS, PHE, and industry.  He is currently working with Dr Chen on DBT and mammographic breast screening, and prostate cancer imaging.

Author Articles
Medical Image Encryption using Chaotic Map Improved Advanced Encryption Standard

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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