Detalhes
Nome
Jorge SilvaCluster
Redes de Sistemas InteligentesCargo
Investigador SéniorDesde
01 janeiro 2014
Nacionalidade
PortugalCentro
Centro de Investigação em Engenharia BiomédicaContactos
+351222094106
jorge.silva@inesctec.pt
2019
Autores
Ferreira, CA; Penas, S; Silva, J; Mendonca, AM;
Publicação
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)
Abstract
2019
Autores
Wanderley, DS; Carvalho, CB; Domingues, A; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;
Publicação
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science
Abstract
2019
Autores
Carvalho, C; Marques, S; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;
Publicação
Lecture Notes in Computer Science - Image Analysis and Recognition
Abstract
2019
Autores
Marques, S; Carvalho, C; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;
Publicação
2019 IEEE International Ultrasonics Symposium (IUS)
Abstract
2018
Autores
Faria, SP; Penas, S; Mendonca, L; Silva, JA; Mendonca, AM;
Publicação
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.
Teses supervisionadas
2019
Autor
Diego Santos Wanderley
Instituição
UP-FEUP
2019
Autor
Alexandre Saraiva Moreira
Instituição
UP-FEUP
2019
Autor
Gustavo Fernando Marques Duarte de Faria
Instituição
UP-FEUP
2017
Autor
Carlos Alexandre Nunes Ferreira
Instituição
UP-FEUP
2016
Autor
Simão Pedro Marques Pinto de Faria
Instituição
UP-FEUP
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