Biomedical signal and image processing methods have recently focused on forming computational tools for clinical decision support. These tools have had a tremendous impact on healthcare and its economics. The success of these methodologies in clinical applications has often been attributed to an interdisciplinary nature that combines knowledge of biology/medicine with methods in computational sciences. The fast growth of monitoring and imaging methods calls for more specialized biomedical signal and image processing algorithms to process the data collected by the existing and emerging patient monitoring and imaging systems. During the last few decades, the vital role of biomedical signal and image processing methods in emerging fields of life sciences such as translational medicine has been further recognized. With this in mind we are editing a special issue of Computational and Mathematical Methods in Medicine, updating current status and advancements of methods in biomedical signal and image processing, in particular their applications in clinical decision support systems. We encourage authors to submit original papers as well as review articles related to advancing the design and clinical applications of these computational methods. In particular, since biomedical signal and image processing are at the junction of computer science, electrical engineering, applied mathematics, medicine, biology, and healthcare technology, we are interested in the latest results of state-of-the-art interdisciplinary projects in the field.

Potential topics include, but are not limited to:

• Novel computational approaches in signal and image processing methods and their applications in computer-aided clinical decision support systems
• Signal and image processing for telemedicine and telesurgery
• Digital and video microscopy, cell analysis, and cytometry
• Processing and analysis of X-ray, CT, NMR, PET, SPECT, digital radiology and mammography, and other imaging systems
• Processing of ECG, EEG, EMG, EOG, blood pressure, and other physiological signals
• Analysis of fMRI for research and clinical applications
• Using biomedical signal and image processing
• Machine learning methods used for biomedical signal and image analysis
• Signal and image processing methods for analysis of genomic and proteomic data, applications in personalized medicine, and clinical decision support systems