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IET Image Processing is a Gold Open Access journal that publishes original research in areas related to the generation, processing and communication of visual information.


IET Image Processing is looking to recruit new Associate Editors

If you are an expert in the image processing area and are interested in supporting the journal in this capacity, you can view our Call for Associate Editors for information on the role and the criteria applicants should meet.

Please contact IET Image Processing Managing Editor Kayleigh Gibson by email if you have any questions or would like to express your interest in the role.


Professor Dimitrios Makris appointed Deputy Editor-in-Chief for Special Issues

We are pleased to announce that IET Image Processing has appointed a Deputy Editor-in-Chief for Special Issues, Professor Dimitrios Makris.

Professor Makris received his 5-year Greek Diploma (equivalent to MEng) from the Aristotle University of Thessaloniki in Greece in 1999 and his PhD from City University London in 2004. He joined Kingston University in 2003 and is currently a professor at the School of Computer Science and Mathematics. His research interests are in the area of computer vision, machine learning and video and motion analysis. He is known for his research on learning scene semantic models, multiple camera surveillance systems and human motion analysis, and he has also worked on other research problems that involve the analysis of multi-dimensional time series, e.g. medical video from contrast-enhanced ultrasound or event-series from neuromorphic vision sensors. He was an elected member (2007-2011) of the BMVA Executive Committee, a member (2014-2016) and the chair (2017-2021) of the steering committee of the IET Vision and Imaging Technical Network.


 

Articles

Open access

Hybrid attention mechanism of feature fusion for medical image segmentation

  •  28 September 2023

Graphical Abstract

Description unavailable

We design a hybrid attention mechanism networks to segment multi-organ in medical image. On the one hand, self-attention and channel attention are integrated to capture global and local features which adapt to the different contrast among organs; on the other hand, a refinement module refine is constructed to refine organ details to further optimize segmentation accuracy. Our model helps to identify organs and boundaries in medical images, significantly improving the segmentation accuracy of medical images.

Open access

High performance image steganography integrating IWT and Hamming code within secret sharing

  •  28 September 2023

Graphical Abstract

Description unavailable

We enhanced involving integer wavelet transform (IWT) steganography with secret-sharing via Hamming code adopting integers coefficients to avoid common rounding errors problem allowing the secret key to be extracted precisely without demanding the original images. The design calculations enjoyed faster processing among available state-of-the-art discrete wavelet transform schemes. It further obtained high-quality steganography making it highly efficient compared to previous schemes, making this contribution in an overall attractive research pioneering position.

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