Special issue: Intelligent signal and image processing in eHealth

Highly technological intelligent solutions based on the appropriate and careful interpretation of medical data, acquired by diagnostic investigations are more and more assuming a key importance in the improvement of health care quality and management. The considerable advances in diagnostic technologies and enhancement of the different modalities have made possible to obtain high-resolution images and signals which are able to provide highly precise information regarding body structure and function, which allow clinicians making more accurate and efficient diagnoses, often in a non-invasive way. As a result, in the last decades, the development of computerised methods for diagnostic data processing and management has attracted a lot of interest and effort within medical imaging and diagnostic radiology, becoming in some cases a practical clinical approach. The basic concept of these methods is to provide a second opinion or a second reader that can aid clinicians in improving the accuracy and consistency of the diagnostic, prognostic and follow-up processes. Actually, the clinical interpretation of diagnostic data and their findings largely depends on the reader's subjective point of view, knowledge and experience. The presence of noise or the vast amount of data, generated by some devices, can make the detection of potential diseases a burdensome task and may cause oversight errors. Hence, computer-aided methods, able to make this interpretation reproducible and consistent, are fundamental for reducing subjectivity while increasing accuracy. Moreover, the amount and complexity of data and information to be analyzed and managed strongly demand for the development of computerised decision aiding systems able to cope with the increasing bulk of clinical data by providing an integrated approach to analysis, foster adherence to guidelines, prevent omissions and disseminate up-to-date specialist knowledge. In this respect, the aim of this Special Issue is to gather new research and application trends in eHealth including intelligent signal and image processing, advanced systems for medical ontologies, medical knowledge discovery, representation and management, efficient clinical decision support systems, multilevel modeling of pathologies, therapy simulation and virtualization of the human physiology; all methods that are becoming an essential component in supporting clinicians' decision making during their clinical routine workflow.