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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:
https://www.cienciavitae.pt//pt/6E1E-F57C-A94D 

Interest
Topics
Details

Details

004
Publications

2020

Automatic Quality Assessment of a Forced Expiratory Manoeuvre Acquired with the Tablet Microphone

Authors
Almeida, R; Pinho, B; Jacome, C; Teixeira, JF; Amaral, R; Goncalves, I; Lopes, F; Pinheiro, AC; Jacinto, T; Paixao, C; Pereira, M; Marques, A; Fonseca, JA;

Publication
IFMBE Proceedings

Abstract
Evaluation of lung function is central to the management of chronic obstructive respiratory diseases. It is typically evaluated with a spirometer by a specialized health professional, who ensures the correct execution of a forced expiratory manoeuvre (FEM). Audio recording of a FEM using a smart device embedded microphone can be used to self-monitor lung function between clinical visits. The challenge of microphone spirometry is to ensure the validity and reliability of the FEM, in the absence of a health professional. In particular, the absence of a mouthpiece may allow excessive mouth closure, leading to an incorrect manoeuvre. In this work, a strategy to automatically assess the correct execution of the FEM is proposed and validated. Using 498 FEM recordings, both specificity and sensitivity attained were above 90%. This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed. © 2020, Springer Nature Switzerland AG.

2020

B-Mode Ultrasound Breast Anatomy Segmentation

Authors
Teixeira, JF; Carreiro, AM; Santos, RM; Oliveira, HP;

Publication
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2020

A Framework for Fusion of T1-Weighted and Dynamic MRI Sequences

Authors
Teixeira, JF; Bessa, S; Gouveia, PF; Oliveira, HP;

Publication
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2020

Personalized 3D Breast Cancer Models with Automatic Image Segmentation and Registration

Authors
BESSA, S; TEIXEIRA, JF; CARVALHO, PH; GOUVEIA, PF; OLIVEIRA, HP;

Publication
Proceedings of 3DBODY.TECH 2020 - 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Online/Virtual, 17-18 November 2020

Abstract

2020

Combined Image-Based Approach for Monitoring the Adherence to Inhaled Medications

Authors
Vieira Marques, P; Teixeira, JF; Valente, J; Pinho, B; Guedes, R; Almeida, R; Jacome, C; Pereira, A; Jacinto, T; Amaral, R; Goncalves, I; Sousa, AS; Couto, M; Pereira, M; Magalhaes, M; Bordalo, D; Silva, LN; Fonseca, JA;

Publication
XV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING - MEDICON 2019

Abstract
The adherence to inhaled controller medications is of critical importance to achieve good clinical results in patients with chronic respiratory diseases. To objectively verify the adherence, a detection tool was previously developed and integrated in the mobile application InspirerMundi, based on image processing methods. In this work, a new approach for enhanced adherence verification was developed. In a first phase template matching is employed to confirm the inhaler positioning and to locate the dose counter. In a second phase Google ML Kit framework is used for the detection of each numerical dose in the dose counter. The proposed approach was validated through a new detection tool pilot implementation, using a set of images collected by patients using the application in their daily life. Performance of each of the two phases was evaluated for a set of commonly used inhaler devices. Promising results were achieved showing the potential of mobile embedded sensors without the need for external devices.

Supervised
thesis

2017

Automated Detection of Anatomical structure keypoints on medical imaging algorithms directed for X-Ray Mammography

Author
Hugo Manuel Soares Oliveira

Institution
UP-FCUP

2017

Automated Detection of Bone structur keypoints on Magnetic Resonance Imaging - Sternum and Clavicules

Author
Beatriz Gonçalves Rocha

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