Yan Chen

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

E-mail: Y.Chen@lboro.ac.uk

Website:

Research Interests: Image Compression, Image Manipulation, Medical Image Computing, Medicine & Healthcare

Biography

Dr. Yan Chen received a PhD degree in Computer Science from Loughborough University. Currently she is a Senior Research Fellow in the Dept. of Computer Science at Loughborough University.  In 2014, She was awarded honorary membership of the Royal College of Radiologists. Her research primarily focuses on precision imaging, spans FFDM, DBT, CESM, MRI and ultrasound imaging in breast imaging, prostate cancer imaging and Lung cancer imaging. More than 50 papers have been published in high impact journals and conferences, including European radiology, Clinical Radiology.  She contributed to a number of projects as PI/Co-I, including projects funded by NIHR, H2020, Public Health England, local and international industry and research institutions.

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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