In the digital era, medical images are transmitted and stored in cloud environments. For this, the environment must be secured and the quality of medical images must be informative. Recently, machine learning and pattern recognition techniques have played an important role in the medical imaging field. With advances in medical imaging, new machine learning and pattern recognition algorithms such as supervised, unsupervised, semi-supervised, and deep learning are being used to solve medical imaging-related problems. The process of medical imaging depends on many physical measurements such as software and hardware. Due to statistical uncertainty in the physical measurements of imaging tools, inevitable problems such as noise, artifacts, blurring, and so on are introduced in medical images. Therefore, new algorithms and applications are required to enhance the quality of medical images. The preservation of important features of the image such as edges, corners, and other sharp structures is a challenging task. Therefore, new imaging modalities/methodologies and new algorithms/applications are required in medical imaging and its applications such as computed tomography (CT) imaging and reconstruction, magnetic resonance imaging (MRI) imaging and fusion, secure multimodality medical imaging, positron emission tomography (PET) imaging and restoration, ultrasound imaging and despeckling.

The objective of this Special Issue is to concentrate on all aspects of state-of-the-art developments, methods, and future research directions related to medical image security and forensics based on advanced computational intelligence systems such as deep/machine learning and pattern recognition techniques. Original research and review papers are welcome.

Potential topics include but are not limited to the following:

• Medical Image reconstruction in secure domains
• Multi-modality medical image fusion in secure domains
• Deep learning-based Medical image analysis and enhancement in secure domains
• Intelligent steganalysis for medical images based on deep learning
• Medical Image forensics based on deep learning
• Robust, fragile, and semi-fragile watermarking for medical image processing
• Reversible data hiding for medical image processing
• Intelligent medical image processing in encrypted domains
• Tampering detection in multiple operator chains
• Visual cryptography and secret image sharing