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Biomedical Engineering Research

At C-BER our main goals are the creation of interdisciplinary knowledge enabling innovation and technology transfer with economic impact; and also the development of products, tools and methods for the prevention and early detection of different types of diseases, aging-related impairments, or for human rehabilitation, physical therapy or functional assessment.

We also seek to contribute to the development of advanced neuro-technologies at the frontier of engineering and neurology, and to promote strategic partnerships with clinical partners, research institutes, and fostering international cooperation.

Our R&D activity is developed in three different areas: BioInstrumentation, Biomedical Imaging and NeuroEngineering.

Latest News
Networked Intelligent Systems

Innovation in breast cancer diagnosis published by INESC TEC

Breast cancer is currently one of the main causes of death by oncological disease worldwide.

18th June 2017

INESC TEC researchers output highlighted in Japan

Pedro Costa, CMU-Portugal, and Aurélio Campilho, coordinator of the INESC TEC’s Centre for Biomedical Engineering Research (C-BER), presented a scientific paper at the International Conference on Image Analysis Applications (IAPR MVA 2017), and it was highlighted with some special references.

24th May 2017

INESC TEC creates 1st 3D video system in the world to help patients with epilepsy

A team of researchers from INESC TEC’s Centre for Biomedical Engineering Research (C-BER), led by João Paulo Cunha, have created the first system in the world to use 3D video technology to extract body movements during epileptic seizures. This new system can help health professionals to diagnose patients and define therapies, not only for epilepsy patients, but also for patients with other neurological illnesses, such as Parkinson’s.

03rd February 2016

INESC TEC poster awarded in Germany

A poster co-authored by João Paulo Cunha, coordinator of INESC TEC’s Centre for Biomedical Engineering Research (C-BER), won 2nd place for best poster as part of the 23rd Annual Meeting of the German Society for Sleep Research and Sleep Medicine (DGSM), which took place in Mainz, Germany, on 4 December.

21st December 2015

INESC TEC supports international conference on image analysis and recognition

INESC TEC is supporting the 13th edition of ICIAR 2016 - International Conference on Image Analysis and Recognition, which takes place between 13 and 15 July 2016 in Póvoa de Varzim, Portugal.

16th December 2015

Interest Topics

Featured Projects


Automatic Detection, Segmentation and Classification of Pulmonary Nodules System in Computed Tomography Images



Image Analysis and Machine Learning Platform for Innovation in Diabetic Retinopathy Screening



Projeto Vital Sticker no âmbito do Contrato Programa



NanoSTIMA – Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics



NanoSTIMA – Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics



SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics



VR2Market: Towards a Mobile Wearable Health Surveillance Product for First Response and other Hazardous Professions



Spatio-temporal components of the processing of sensorial and motor information



Inteligent Eco Driving and Fleet Management



Human motor re-learning by sensor information fusion



Intelligent management of critical events of stress, fatigue and smoke intoxication in forest firefighting



Movement Disorders in Autistic Spectrum Disorders



Interactive system for digital content consumers




Biomedical Imaging Laboratory

Neuroengineering and Advanced Human Sensing Laboratory

BioInstrumentation Lab


C-BER Publications

View all Publications


Skin temperature of the foot: A comparative study between familial amyloid polyneuropathy and diabetic foot patients

Seixas, A; do Carmo Vilas Boas, M; Carvalho, R; Coelho, T; Ammer, K; Vilas Boas, JP; Vardasca, R; Cunha, JPS; Mendes, J;

Lecture Notes in Computational Vision and Biomechanics

Skin temperature regulation is dependant of the autonomic nervous system function, which may be impaired in patients with neuropathy. Studies reporting thermographic assessment of patients with established diagnosis of Diabetic Foot (DF) are scarce but this information is completely absent in patients suffering from Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). The aim of this study is to compare skin temperature distribution in patients with DF and TTR-FAP. Thermograms of the dorsal and plantar surfaces were compared. Skin temperature was higher in the diabetic foot group and differences were statistically significant (p < 0.05) in both regions of interest. © 2018, Springer International Publishing AG.


Retinal image quality assessment by mean-subtracted contrast-normalized coefficients

Galdran, A; Araújo, T; Mendonça, AM; Campilho, A;

Lecture Notes in Computational Vision and Biomechanics

The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost. © 2018, Springer International Publishing AG.


Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach

Paiva, JS; Cardoso, J; Pereira, T;


Objective: The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. Materials and methods: The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39 pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results and discussion: SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917 +/- 0.0024 and a F-Measure of 0.9925 +/- 0.0019, in comparison with ANN, which reached the values of 0.9847 +/- 0.0032 and 0.9852 +/- 0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. Conclusion: The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW.


3D mapping of choroidal thickness from OCT B-scans

Faria, SP; Penas, S; Mendonça, L; Silva, JA; Mendonça, AM;

Lecture Notes in Computational Vision and Biomechanics

The choroid is the middle layer of the eye globe located between the retina and the sclera. It is proven that choroidal thickness is a sign of multiple eye diseases. Optical Coherence Tomography (OCT) is an imaging technique that allows the visualization of tomographic images of near surface tissues like those in the eye globe. The automatic calculation of the choroidal thickness reduces the subjectivity of manual image analysis as well as the time of large scale measurements. In this paper, a method for the automatic estimation of the choroidal thickness from OCT images is presented. The pre-processing of the images is focused on noise reduction, shadow removal and contrast adjustment. The inner and outer boundaries of the choroid are delineated sequentially, resorting to a minimum path algorithm supported by new dedicated cost matrices. The choroidal thickness is given by the distance between the two boundaries. The data are then interpolated and mapped to an infrared image of the eye fundus. The method was evaluated by calculating the error as the distance from the automatically estimated boundaries to the boundaries delineated by an ophthalmologist. The error of the automatic segmentation was low and comparable to the differences between manual segmentations from different ophthalmologists. © 2018, Springer International Publishing AG.


Analysis and quantification of upper-limb movement in motor rehabilitation after stroke

Silva, RM; Sousa, E; Fonseca, P; Pinheiro, AR; Silva, C; Correia, MV; Mouta, S;

Biosystems and Biorobotics

It is extremely difficult to reduce the relations between the several body parts that perform human motion to a simplified set of features. Therefore, the study of the upper-limb functionality is still in development, partly due to the wider range of actions and strategies for motor execution. This, in turn, leads to inconsistent upper-limb movement parameterization. We propose a methodology to assess and quantify the upper-limb motor execution. Extracting key variables from different sources, we intended to quantify healthy upper-limb movement and use these parameters to quantify motor execution during rehabilitation after stroke. In order to do so, we designed an experimental setup defining a workspace for the execution of the action recording kinematic data. Results reveal an effect of object and instruction on the timing of upper-limb movement, indicating that the spatiotemporal analysis of kinematic data can be used as a quantification parameter for motor rehabilitation stages and methods. © Springer International Publishing AG 2017.

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