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About

About

João Teixeira was born in Porto, Portugal, in 1991. He received his M.Sc. in Electrical and Computers Engineering at the Faculty of Engineering of University of Porto, Portugal, in 2014. In 2014, he started his Ph.D. studies at Faculty of Sciences, University of Porto, Portugal, under the joint program in Informatics (MAP-i). He has been working since 2015 as a researcher at INESC TEC, an R&D institute affiliated to University of Porto, in the Visual Computing and Machine Intelligence Group (VCMI), and the Breast Research Group. João Teixeira has a standing collaboration and has been consulting for a R&D department of the Faculty of Medicine (FMUP), the Center for Health Technology and Services Research (CINTESIS), since 2013.
His main research interests include computer vision, image processing, signal processing, with particular interest in medical applications (Breast cancer and Respiratory conditions) and m-health initiatives.

For further information please consult the CV:
http://www.degois.pt/visualizador/curriculum.jsp?key=3395739315417314 

Interest
Topics
Details

Details

001
Publications

2018

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Authors
Zolfagharnasab, H; Bessa, S; Oliveira, SP; Faria, P; Teixeira, JF; Cardoso, JS; Oliveira, HP;

Publication
Sensors

Abstract

2017

Multi-modal Complete Breast Segmentation

Authors
Zolfagharnasab, H; Monteiro, JP; Teixeira, JF; Borlinhas, F; Oliveira, HP;

Publication
Pattern Recognition and Image Analysis - 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings

Abstract
Automatic segmentation of breast is an important step in the context of providing a planning tool for breast cancer conservative treatment, being important to segment completely the breast region in an objective way; however, current methodologies need user interaction or detect breast contour partially. In this paper, we propose a methodology to detect the complete breast contour, including the pectoral muscle, using multi-modality data. Exterior contour is obtained from 3D reconstructed data acquired from low-cost RGB-D sensors, and the interior contour (pectoral muscle) is obtained from Magnetic Resonance Imaging (MRI) data. Quantitative evaluation indicates that the proposed methodology performs an acceptable detection of breast contour, which is also confirmed by visual evaluation. © Springer International Publishing AG 2017.

2017

Spacial Aliasing Artefact Detection on T1-Weighted MRI Images

Authors
Teixeira, JF; Oliveira, HP;

Publication
Pattern Recognition and Image Analysis - 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings

Abstract
Magnetic Resonance Imaging (MRI) exams suffer from undesirable structure replicating and overlapping effects on certain acquisition settings. These are called Spatial Aliasing Artefacts (SAA) and their presence interferes with the segmentation of other anatomical structures. This paper addresses the segmentation of the SAA in T1-weighted MRI image sets, in order to effectively remove their influence over the legitimately positioned body structures. The proposed method comprises an initial thresholding, employing the Triangle method, an aggregation of neighboring voxels through Region Growing. Further refinement of the objects contour is obtained with Convex Hull and a Minimum Path algorithm applied to two orthogonal planes (Sagittal and Axial). Some experiments concerning the extension of the pipeline used are reported and the results seem promising. The average contour distance concerning the Ground Truth (GT) rounds 2.5 mm and area metrics point out average overlaps above 64% with the GT. Some issues concerning the fusion between the output from the two planes are to be perfected. Nevertheless, the results seems sufficient to neutralize the influence of SAA and expedite the downstream anatomical segmentation tasks. © Springer International Publishing AG 2017.

2015

Automatic Analysis of Lung Function Based on Smartphone Recordings

Authors
Teixeira, JF; Teixeira, LF; Fonseca, J; Jacinto, T;

Publication
BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, BIOSTEC 2015

Abstract
Over 250 million people, worldwide, are affected by chronic lung conditions such as Asthma and COPD. These can cause breathlessness, a harsh decrease in quality of life and, if left undetected or not properly managed, even death. In this paper, we approached part of the lines of development suggested upon earlier work. This concerned the development of a system design for a smartphone lung function classification app, which would only use recordings from the built-in microphone. A more systematic method to evaluate the relevant combinations of methods was devised and an additional set of 44 recordings was used for testing purposes. The previous 101 were kept for training the models. The results enabled to further reduce the signal processing pipeline leading to the use of 6 envelopes, per recording, half of the previous amount. An analysis of the classification performances is provided for both previous tasks: differentiation into Normal from Abnormal lung function, and between multiple lung function patterns. The results from this project encourage further development of the system.

2015

Automatic Distinction of Fernando Pessoas' Heteronyms

Authors
Teixeira, JF; Couto, M;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE

Abstract
Text Mining has opened a vast array of possibilities concerning automatic information retrieval from large amounts of text documents. A variety of themes and types of documents can be easily analyzed. More complex features such as those used in Forensic Linguistics can gather deeper understanding from the documents, making possible performing difficult tasks such as author identification. In this work we explore the capabilities of simpler Text Mining approaches to author identification of unstructured documents, in particular the ability to distinguish poetic works from two of Fernando Pessoas' heteronyms: 'Alvaro de Campos and Ricardo Reis. Several processing options were tested and accuracies of 97% were reached, which encourage further developments.

Supervised
thesis

2016

A Multi-modal Approach for Breast Imaging Analysis and Surgery Planning

Author
João Pedro Fonseca Teixeira

Institution
UP-FEUP

2015

Conceção e desenvolvimento de uma aplicação móvel para monitoramento da tosse em crianças

Author
José Manuel da Silva Fernandes

Institution
UP-FMUP