In modern healthcare, biomedical signal and image play an important role throughout the ‎entire clinical process from diagnostics and treatment planning to surgical procedures and ‎follow up studies. They are often designate to the set of signals (e.g. electrocardiogram, ‎electroneurogram, electromyogram, phonocardiogram, speech signal, etc.) and images ‎‎(e.g. computed tomography, magnetic resonance imaging, ultrasound, endoscopy, etc.) ‎that are acquired to capture internal aspect of the body. With the increasing amount of ‎patient data, new challenges and opportunities arise for different phases of the clinical ‎routine, such as diagnosis, surgery and therapy. The enormous socio-economic importance ‎of research and innovation in this field to the kingdom is of high importance‎.

Over the past years significant progress has been made in the field of biomedical signal and ‎image analysis, both on methodological and on application level.Despite ‎this progress there are still many challenges to meet in order to achieve systems that are ‎more robust, more accurate and more intuitive to interact with. We envisage that ‎incorporation of the computational intelligent techniques with the existing state of art ‎methodologies would be useful to circumvent many of these challenges. Special attention is ‎required to learn the non-static (e.g. deformable, non-periodic) patterns of underlying ‎physiological or anatomical structures. This will create models of different normal and ‎abnormal patterns, the knowledge could be useful for decision support system, and ‎computer aided diagnosis (CAD). However, algorithmic complexity as well as robustness ‎and speed requirements pose significant computational challenges due to the volumetric ‎data produced by modern acquisition modalities, the non-rigid nature of object motion and ‎deformation, and the statistical variation of both the underlying normal and abnormal ‎ground truth. Thus, for the development of intelligent methods of biomedical signal and ‎image analysis we need efficient solution of underlying basic research problem such as ‎model based segmentation; morphological and functional feature extraction; incorporation ‎of suitable learning method for non-rigid patterns; visualization and classification.‎

Research projects include: 
• Develop a smart cardiovascular remote monitoring system ‎
• Develop an intelligent solution for 3-D ct colonography ‎
• Develop tools and methods for lung nodule detection 
• Develop a system for voice pathology detection ‎
• Develop a system for scanning the human jaw 
‎ • Develop a secured biometric system based on physiological signals 

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