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About

About

My name is Ana Maria Mendonça and I am currently Associate Professor at the Department of Electrical and Computer Engineering (DEEC) of the Faculty of Engineering of the University of Porto (FEUP), where I got my PhD in 1994. I was a researcher at the Institute for Biomedical Engineering (INEB) until 2014, but since 2015 I am a senior researcher at INESC TEC.

For several years, I was a member of the Executive council of DEEC and at present I am the Vice-Dean of FEUP. At INEB, I was a member of the Board of Directors and afterwards President of the Board. I was an elected member of the pedagogical council of FEUP and currently I am also a member of the scientific council. I was the Director of the Master in Biomedical Engineering from 2009 to 2014 and I am a member of the scientific committee of the Doctoral Programme in Biomedical Engineering.

I have been collaborating as a research and also as responsible in several research projects, mostly dedicated to the development of image analysis and classification methodologies aiming at extracting essential information from medical images in order to support the diagnosis process. Past work has been mostly devoted to three main areas: retinal pathologies, lung diseases and genetic disorders, but ongoing work is mainly focused on the development of Computer-Aided Diagnosis systems in Ophthalmology and Radiology.

Interest
Topics
Details

Details

Publications

2018

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

Authors
Galdran, A; Araujo, T; Mendonca, AM; Campilho, A;

Publication
Lecture Notes in Computational Vision and Biomechanics

Abstract
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.

2018

3D mapping of choroidal thickness from OCT B-scans

Authors
Faria, SP; Penas, S; Mendonca, L; Silva, JA; Mendonca, AM;

Publication
Lecture Notes in Computational Vision and Biomechanics

Abstract
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.

2018

Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images

Authors
Araujo, T; Mendonca, AM; Campilho, A;

Publication
PLOS ONE

Abstract

2018

Creation of Retinal Mosaics for Diabetic Retinopathy Screening: A Comparative Study

Authors
Melo, T; Mendonça, AM; Campilho, A;

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

Abstract
The creation of retinal mosaics from sets of fundus photographs can significantly reduce the time spent on the diabetic retinopathy (DR) screening, because through mosaic analysis the ophthalmologists can examine several portions of the eye at a single glance and, consequently, detect and grade DR more easily. Like most of the methods described in the literature, this methodology includes two main steps: image registration and image blending. In the registration step, relevant keypoints are detected on all images, the transformation matrices are estimated based on the correspondences between those keypoints and the images are reprojected into the same coordinate system. However, the main contributions of this work are in the blending step. In order to combine the overlapping images, a color compensation is applied to those images and a distance-based map of weights is computed for each one. The methodology is applied to two different datasets and the mosaics obtained for one of them are visually compared with the results of two state-of-the-art methods. The mosaics obtained with our method present good quality and they can be used for DR grading. © 2018, Springer International Publishing AG, part of Springer Nature.

2018

Deep Convolutional Artery/Vein Classification of Retinal Vessels

Authors
Meyer, MI; Galdran, A; Costa, P; Mendonça, AM; Campilho, A;

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

Abstract
The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of 96 % on large caliber vessels, and an overall accuracy of 84 %. © 2018, Springer International Publishing AG, part of Springer Nature.

Supervised
thesis

2017

Quantitative assessment of Central Serous Chorioretinopathy in Angiographic sequences of retinal images

Author
Carlos Alexandre Nunes Ferreira

Institution
UP-FEUP

2016

Estimation of Vessel Caliber in Retinal Images

Author
Teresa Manuel Sá Finisterra Araújo

Institution
UP-FEUP

2016

Detection of red lesions associated with Diabetic Retinopathy

Author
Tânia Filipa Fernandes Melo

Institution
UP-FEUP

2016

Estimation of choroidal thickness in OCT images

Author
Simão Pedro Marques Pinto de Faria

Institution
UP-FEUP

2015

Advanced Image Analysis for the Assessment of Retinal Vascular Changes

Author
Behdad Dasht Bozorg

Institution
UP-FEUP