2018
Authors
Oliveira, LM; Carvalho, MI; Nogueira, EM; Tuchin, VV;
Publication
JOURNAL OF BIOPHOTONICS
Abstract
Skeletal muscle dispersion and optical clearing (OC) kinetics were studied experimentally to prove the existence of the refractive index (RI) matching mechanism of OC. Sample thickness and collimated transmittance spectra were measured during treatments with glucose (40%) and ethylene glycol (EG; 99%) solutions and used to obtain the time dependence of the RI of tissue fluids based on the proposed theoretical model. Calculated results demonstrated an increase of RI of tissue fluids and consequently proved the occurrence of the RI matching mechanism. The RI increase was observed for the wavelength range between 400 and 1000 nm and for the 2 probing molecules explored. We found that for 30 min treatment with 40% glucose and 99% EG, RI of sarcoplasm plus interstitial fluid was increased at 800 nm from 1.328 to 1.348 and from 1.328 to 1.369, respectively.
2018
Authors
Mendonca, L; Faria, S; Penas, S; Silva, J; Mendonca, AM;
Publication
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
Abstract
2018
Authors
Rocha, AP; Pereira Choupina, HMP; Vilas Boas, MD; Fernandes, JM; Silva Cunha, JPS;
Publication
PLOS ONE
Abstract
Human gait analysis provides valuable information regarding the way of walking of a given subject. Low-cost RGB-D cameras, such as the Microsoft Kinect, are able to estimate the 3-D position of several body joints without requiring the use of markers. This 3-D information can be used to perform objective gait analysis in an affordable. portable, and non-intrusive way. In this contribution, we present a system for fully automatic gait analysis using a single RGB-D camera, namely the second version of the Kinect. Our system does not require any manual intervention (except for starting/stopping the data acquisition), since it firstly recognizes whether the subject is walking or not, and identifies the different gait cycles only when walking is detected. For each gait cycle, it then computes several gait parameters, which can provide useful information in various contexts, such as sports, healthcare, and biometric identification. The activity recognition is performed by a predictive model that distinguishes between three activities (walking, standing and marching), and between two postures of the subject (facing the sensor, and facing away from it). The model was built using a multilayer perceptron algorithm and several measures extracted from 3-D joint data, achieving an overall accuracy and F-1 score of 98%. For gait cycle detection, we implemented an algorithm that estimates the instants corresponding to left and right heel strikes, relying on the distance between ankles, and the velocity of left and right ankles. The algorithm achieved errors for heel strike instant and stride duration estimation of 15 +/- 25 ms and 1 +/- 29 ms (walking towards the sensor), and 12 +/- 23 ms and 2 +/- 24 ms (walking away from the sensor ) Our gait cycle detection solution can be used with any other RGB-D camera that provides the 3-D position of the main body joints.
2018
Authors
Rodrigues*, S; Paiva, JS; Dias, D; Pereira, T; Cunha, JPS;
Publication
The European Proceedings of Social and Behavioural Sciences
Abstract
2018
Authors
Machado, M; Aresta, G; Leitao, P; Carvalho, AS; Rodrigues, M; Ramos, I; Cunha, A; Campilho, A;
Publication
2018 1ST INTERNATIONAL CONFERENCE ON GRAPHICS AND INTERACTION (ICGI 2018)
Abstract
Lung cancer diagnosis is made by radiologists through nodule search in chest Computed Tomography (CT) scans. This task is known to be difficult and prone to errors that can lead to late diagnosis. Although Computer-Aided Diagnostic (CAD) systems are promising tools to be used in clinical practice, experienced radiologists continue to perform better diagnosis than CADs. This paper proposes a methodology for characterizing the radiologist's gaze during nodules search in chest CT scans. The main goals are to identify regions that attract the radiologists' attention, which can then be used for improving a lung CAD system, and to create a tool to assist radiologists during the search task. For that purpose, the methodology processes the radiologists' gaze and their mouse coordinates during the nodule search. The resulting data is then processed to obtain a 3D gaze path from which relevant attention studies can be derived. To better convey the found information, a reference model of the lung that eases the communication of the location of relevant anatomical/pathological findings is also proposed. The methodology is tested on a set of 24 real-practice gazes, recorded via an Eye tracker, from 3 radiologists.
2018
Authors
Paiva, JS; Cardoso, J; Pereira, T;
Publication
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Abstract
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.
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