cberC-BER: Center for Biomedical Engineering Research 

Together with J. P. Cunha, I am coordinating the research INESC TEC Center, C-BER.   The mission of C-BER is to promote knowledge through applied research, advanced training and innovation in Biomedical Engineering, with the major goals :

1) to create interdisciplinary knowledge enabling the innovation and tech. transfer with economic impact;

2) to develop technological products, tools and methods for the prevention and early detection of different types of cancer (lung, breast), major prevalent diseases (diabetes, hypertension, cardiovascular), aging related impairments (Alzheimer, Parkinson or macular degeneration), or for human rehabilitation,  physiotherapy or functional assessment.

4) to contribute to the development of advanced neurotechnologies at the frontier of engineering and neurology.

5) to promote strategic partnerships with: a) other centers of INESC TEC; b) clinical partners, from the main hospitals in the region; c)  National and International research institutes and Companies.

C-BER research is organized in the three Labs: BioInstrumentation Lab, Biomedical Imaging Lab and NeuroEngineering Lab. My research activities are in the Biomaging Lab.

Biomedical Imaging Lab

The main focus of the Biomedical Imaging Lab is the development of advanced image processing and analysis methodologies, particularly medical and biological images, with the aim of creating computer-aided diagnosis tools to support medical decision making. The research activities at the Lab use several imaging modalities addressing different clinical problems, namely: analysis of eye fundus images for early detection of prevalent eye pathologies, measurement of macro vascular markers in ultrasound images, early detection and characterisation of lung pathologies, and content-based image retrieval using lung CT image data. The main areas of research are:

Ophthalmology CAD – Design innovative methods and tools for the early detection/characterization of prevalent eye pathologies, supported by the detection of characteristic features and lesions in fundus layers, as retina and choroid, and the combination of data from distinct modalities (retinography, angiography, OCT).

Lung CAD – To create CAD and content-based image retrieval systems for pulmonary pathologies, relying on previous experience in lung nodule (particularly subtle nodules or non-solid nodules as ground glass opacities) detection and characterization of interstitial lung disease.

Ultrasound CAD: We address US applications in Ginecology/Obstetrics and Vascular Imaging, by extracting several image information, such as macrovascular markers of arteriosclerosis as the intima-media thickness and plaque burden from carotid US images, or endometrium thickness.