M
María J. Carreira
Researcher at University of Santiago de Compostela
Publications - 61
Citations - 959
María J. Carreira is an academic researcher from University of Santiago de Compostela. The author has contributed to research in topics: Gabor wavelet & Hough transform. The author has an hindex of 13, co-authored 53 publications receiving 822 citations. Previous affiliations of María J. Carreira include University of Santiago, Chile & University of A Coruña.
Papers
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Journal ArticleDOI
Computer-aided diagnosis: a neural-network-based approach to lung nodule detection
TL;DR: A computer-aided diagnosis system, based on a two-level artificial neural network (ANN) architecture, trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on digitized chest radiographs.
Journal ArticleDOI
Retinal verification using a feature points-based biometric pattern
TL;DR: An efficient method for persons authentication is showed, a deep analysis of similarity metrics performance is presented and a set of feature points representing landmarks in the retinal vessel tree of the biometric system is described.
Book ChapterDOI
A Snake for Retinal Vessel Segmentation
TL;DR: An innovative methodology to detect the vessel tree in retinal angiographies inspired in the classical snake but incorporating domain specific knowledge, such as blood vessels topological properties, is presented.
Proceedings ArticleDOI
Retinal vessel tree segmentation using a deformable contour model
TL;DR: An improved version of the specific methodology to detect the vessel tree in retinal angiographies, inspired in the classical snake but incorporating domain specific knowledge, such as blood vessels topological properties is presented.
Journal ArticleDOI
A snake for CT image segmentation integrating region and edge information
TL;DR: This work presents a deformable contour method for the problem of automatically delineating the external bone contours from a set of CT scan images and introduces a new region potential term and an edge focusing strategy that diminish the problems that the classical snake method presents when it is applied to the segmentation of CT images.