About this Research Topic
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. Following past years' tendency, it is foreseen that these methodologies will increase in complexity and will also have an increasing range of applications. In the last decade, the discipline has undergone a remarkable evolution, with the availability of large volumes of data and the increase in computational power. Deep learning is gaining ground and is increasing in relevance in the medical image processing field. Despite all the recent advances in medical image analysis in the last decades, there is still a significant uncharted territory and much to be understood in this field mainly due to the daily advances in medical imaging devices. Medical images are typically noisy images due to low contrast-to-noise ratio and low spatial and temporal resolution. These characteristics introduce uncertainty in image analysis making it more difficult to quantify information content. Information theory provides theoretical background and tools to quantify information content, and uncertainty, in medical images.
This Research Topic aims at presenting the latest advances in medical image processing methodologies and their contribution to the medical field and leveraging research on medical image processing. This Topic intends to cover the development and implementation of new medical image-based algorithms and strategies using biomedical image datasets. The overall aim of this Research Topic is to disclose scientific knowledge on medical image processing and its impacts on the community.
To gather further insights in the field of medical image analysis and processing, we welcome articles addressing, but not limited to, the following themes:
- Development of new algorithms for medical image processing
- Applications of deep learning in medical image analysis
- Enhancements in image quality and resolution
- Quantification of information content and uncertainty in medical images
- Integration of multi-modal imaging data
- Real-time processing and visualization techniques
- Clinical applications and case studies
- Advances in imaging hardware and software tools
This Research Topic aims at presenting the latest advances in medical image processing methodologies and their contribution to the medical field and leveraging research on medical image processing. This Topic intends to cover the development and implementation of new medical image-based algorithms and strategies using biomedical image datasets. The overall aim of this Research Topic is to disclose scientific knowledge on medical image processing and its impacts on the community.
To gather further insights in the field of medical image analysis and processing, we welcome articles addressing, but not limited to, the following themes:
- Development of new algorithms for medical image processing
- Applications of deep learning in medical image analysis
- Enhancements in image quality and resolution
- Quantification of information content and uncertainty in medical images
- Integration of multi-modal imaging data
- Real-time processing and visualization techniques
- Clinical applications and case studies
- Advances in imaging hardware and software tools
Keywords: Segmentation, Image registration, Image denoising, Image visualization, feature extraction and classification, Virtual and augmented reality, Deep Learning, Machine Learning, Network physiology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
