UNIVERSITY OF PIRAEUS

The Computational Biomedicine Research Lab, within the Department of Digital Systems in University of Piraeus consists of faculty members with PhDs in the area of biomedical engineering and machine learning, who supervise postgraduate research work of highly qualified graduate engineers. The Lab is entirely devoted to research activities and its members are highly qualified in their corresponding research fields with extensive theoretical and computational experience.

The Computational Biomedicine Laboratory in the University of Piraeus has innovative scientific activity in the fields of biomedical informatics and electronic health with a number of research projects and publications in journals and conferences of global scope. The Computational Biomedicine Laboratory is preoccupied with biomedical engineering and technology, medical data processing and analysis, medical image analysis and pattern recognition, AI explainability, medical data transfer and storage in the Internet of Things, creating environments for the support of independent living. The work in the laboratory involves the implementation of advanced intelligent information systems combining the above features as well as research in advanced user experience (UX) technologies aiming at the remote support of health professionals and improving the quality of life of citizens. The laboratory has in operation relevant platforms, application and data for the above operations.

The group has great expertise in processing of medical image and video data (i.e. data segmentation and annotation, coding, pattern recognition and image reconstruction) and biosignal processing (pattern recognition, coding and patient status interpretation). A number of methodologies and tools have been developed by the group that allows proper coding of medical data (e.g., Region of Interest coding in medical images and videos using advanced progressing wavelets compression and H.264-based techniques), image segmentation and classification techniques (e.g., microscopy images segmentation and quantification and skin melanoma detection and classification). Apart from the aforementioned tools, the group has established a repository of medical data consisting mainly of medical images of various modalities.

The group has also significant research work in Telemedicine and Context Aware Systems. In rapidly changing scenarios, such as the ones considered in the fields of mobile, pervasive, or ubiquitous computing, systems have to adapt their behavior based on the current conditions and the dynamicity of the environment they are immerse in. The group has developed mechanisms and methodologies for context status modelling interpretation (utilizing ontologies and rule engines) and medical content proper adaptation. Great expertise has also been gained in patient status awareness using body sensor networks and advanced pattern recognition techniques that are specifically used for detecting patient body falls and related distress situations. Specific applications for Teledermatology, Teleradiology and Collaborative Platforms for the provision of physician’s distant interaction and 2nd opinion have been developed. In addition the group has participated in the development of a Grid based platform capable of statistical processing of large-scale biological data, e.g., DNA microarray experiment data (www.grissom.gr). The latter experiments are becoming a standard technique in order to examine patterns of gene expression. As this technology matures and the cost drops significantly, the amount of experimental data produced by laboratories around the world constantly increases, leading to the problem of finding computational and storage resources. The group has developed methodologies and tools for performing statistical filtering of genes expressions, gene annotation and clustering. The parallelization of the latter tools has allowed faster and more efficient execution of DNA microarray data statistical filtering.

Apart from the development of such tools, the Lab maintains a database of anonymous medical data, consisting mainly of medical images of various modalities.
The Laboratory  serves the following educational and research subjects:

• Biomedical Engineering and Technology

• Biosignal Processing

• Analysis of Biomedical Images

• Pattern Recognition in Medical Data

• Biosensors Technologies

• Wireless Sensor Networks and Internet of Thing

• Environments for independent living (assistive environments)

• Living Labs (Living Labs)

• Bioinformatics

• Augmented Reality