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About

I’m an Assistant Professor at the University of Trás-os-Montes and Alto Douro (UTAD), Portugal since 1996 and I teach  Networks and Security. I graduated in 1993 and started working at STCP, the Public Transport's operator of Porto. I finish my master's thesis in 1998, and obtained my doctorate in 2005, in the area of computer vision related to control of automated guided vehicles.  I’m a member of Centre for Biomedical Engineering Research (C-BER), in the research center INESC TEC since 2014. My investigation is in Electrical Engineering, Electronics & Computers, with a particular focus in machine learning and biomedical image processing.

Interest
Topics
Details

Details

003
Publications

2019

An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans

Authors
Shakibapour, E; Cunha, A; Aresta, G; Mendonca, AM; Campilho, A;

Publication
Expert Systems with Applications

Abstract
This paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative – LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R-value = 92.16% with a significance level of 5%. © 2018 Elsevier Ltd

2019

Convolutional Neural Network Architectures for Texture Classification of Pulmonary Nodules

Authors
Ferreira, CA; Cunha, A; Mendonça, AM; Campilho, A;

Publication
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

Abstract

2019

Characterization of Water and Energy Consumptions at the End Use Level in Rural and Urban Environments: Preliminary Results of the ENERWAT Project

Authors
Matos, C; Cunha, A; Pereira, F; Gonçalves, A; Silva, E; Pereira, S; Bentes, I; Faria, D; Briga-Sá, A;

Publication
Urban Science

Abstract
The characterization of water and energy consumptions is essential in order to define strategies for their rational use. The way these resources are used in households is the path for efficient and rational management, interdependent from each other. It is believed that there are significant differences between the patterns of water and energy consumption in rural and urban areas, where influencing factors should also be identified. This article aims to provide some preliminary results of a research project named ENERWAT, with the main goal to characterize the relation between water and energy consumption at the end use level for urban and rural environments. One of the goals of the aforementioned project was the design, application, and results analysis of a survey, in order to find the main differences in the water and energy consumptions at the end use level and the factors that influence it in urban and rural households. A total of 245 households participated in the research during 2016 (110 urban dwellings and 135 rural), responding to questions on their family composition, dwellings characterization, water and energy consumption habits, and conservation behaviors of these resources. The project also includes the instrumentation and monitoring of dwellings in rural and urban environments to quantify the water consumption and related energy consumption. This stage is still in progress and includes in situ measurements of nine different households (four in rural and five in urban environments) during at least one year. In this article, some of the results obtained by the survey application and the in situ measurements are presented. Despite the large number of data and the associated complexity, it can be concluded that the joint analysis of the results allows identification of a connection between water and energy consumption, as well as a household’s consumption patterns.

2019

Convolutional neural network architectures for texture classification of pulmonary nodules

Authors
Ferreira, CA; Cunha, A; Mendonça, AM; Campilho, A;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Lung cancer is one of the most common causes of death in the world. The early detection of lung nodules allows an appropriate follow-up, timely treatment and potentially can avoid greater damage in the patient health. The texture is one of the nodule characteristics that is correlated with the malignancy. We developed convolutional neural network architectures to classify automatically the texture of nodules into the non-solid, part-solid and solid classes. The different architectures were tested to determine if the context, the number of slices considered as input and the relation between slices influence on the texture classification performance. The architecture that obtained better performance took into account different scales, different rotations and the context of the nodule, obtaining an accuracy of 0.833 ± 0.041. © Springer Nature Switzerland AG 2019.

2019

Radiologists' Gaze Characterization during Lung Nodule Search in Thoracic CT

Authors
MacHado, M; Aresta, G; Leitão, P; Carvalho, AS; Rodrigues, M; Ramos, I; Cunha, A; Campilho, A;

Publication
Proceedings - ICGI 2018: International Conference on Graphics and Interaction

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 IEEE.

Supervised
thesis

2017

Deteção e segmentação de sangramentos em imagens gastrointestinais de cápsulas endoscópicas

Author
Paulo Jorge Simões Coelho

Institution
UTAD

2017

Detection of lung nodules in computed tomography images

Author
Guilherme Moreira Aresta (aluno FEUP)

Institution
UTAD

2017

Desenvolvimento de uma aplicação android para o Jardim Botanico da UTAD

Author
João Carlos Trindade Moreira

Institution
UTAD

2017

Visual odometer on videos of endoscopic capsules (VEC)

Author
Gil Martins Pinheiro

Institution
UTAD

2016

in computed tomography images"

Author
Guilherme Moreira Aresta

Institution
UTAD