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Browsing C-BER - Indexed Articles in Conferences by Author "Adrian Galdran"
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ItemAdversarial Synthesis of Retinal Images from Vessel Trees( 2017) Costa,Pedro ; Adrian Galdran ; Maria Inês Meyer ; Ana Maria Mendonça ; Aurélio CampilhoSynthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. Here we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality. © Springer International Publishing AG 2017.
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ItemCubic spline regression based enhancement of side-scan sonar imagery( 2017) Al-Rawi,M ; Adrian Galdran ; Isasi,A ; Elmgren,F ; Carbonara,G ; Falotico,E ; Real-Arce,DA ; Rodriguez,J ; Bastos,J ; Pinto,M
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ItemDeep Convolutional Artery/Vein Classification of Retinal Vessels( 2018) Maria Inês Meyer ; Adrian Galdran ; Costa,P ; Ana Maria Mendonça ; Aurélio Campilho ; 6825 ; 6071 ; 6381 ; 6835The 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.
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ItemA Deep Neural Network for Vessel Segmentation of Scanning Laser Ophthalmoscopy Images( 2017) Maria Inês Meyer ; Costa,Pedro ; Adrian Galdran ; Ana Maria Mendonça ; Aurélio CampilhoRetinal vessel segmentation is a fundamental and well-studied problem in the retinal image analysis field. The standard images in this context are color photographs acquired with standard fundus cameras. Several vessel segmentation techniques have been proposed in the literature that perform successfully on this class of images. However, for other retinal imaging modalities, blood vessel extraction has not been thoroughly explored. In this paper, we propose a vessel segmentation technique for Scanning Laser Opthalmoscopy (SLO) retinal images. Our method adapts a Deep Neural Network (DNN) architecture initially devised for segmentation of biological images (U-Net), to perform the task of vessel segmentation. The model was trained on a recent public dataset of SLO images. Results show that our approach efficiently segments the vessel network, achieving a performance that outperforms the current state-of-the-art on this particular class of images. © Springer International Publishing AG 2017.
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ItemAn efficient non-uniformity correction technique for side-scan sonar imagery( 2017) Adrian Galdran ; Isasi,A ; Al-Rawi,M ; Rodriguez,J ; Bastos,J ; Elmgren,F ; Pinto,M
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ItemEnd-to-End Supervised Lung Lobe Segmentation( 2018) Ferreira,FT ; Sousa,P ; Adrian Galdran ; Sousa,MR ; Aurélio Campilho ; 6825 ; 6071
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ItemIllumination Correction by Dehazing for Retinal Vessel Segmentation( 2017) Savelli,B ; Bria,A ; Adrian Galdran ; Marrocco,C ; Molinara,M ; Aurélio Campilho ; Tortorella,F
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ItemA No-Reference Quality Metric for Retinal Vessel Tree Segmentation( 2018) Adrian Galdran ; Costa,P ; Bria,A ; Teresa Finisterra Araújo ; Ana Maria Mendonça ; Aurélio Campilho ; 6825 ; 6320 ; 6381 ; 6071
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ItemA Pixel-Wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection( 2018) Maria Inês Meyer ; Adrian Galdran ; Ana Maria Mendonça ; Aurélio Campilho ; 6381 ; 6071 ; 6825 ; 6835
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ItemSpatial Enhancement by Dehazing for Detection of Microcalcifications with Convolutional Nets( 2017) Bria,A ; Marrocco,C ; Adrian Galdran ; Aurélio Campilho ; Marchesi,A ; Mordang,JJ ; Karssemeijer,N ; Molinara,M ; Tortorella,FMicrocalcifications are early indicators of breast cancer that appear on mammograms as small bright regions within the breast tissue. To assist screening radiologists in reading mammograms, supervised learning techniques have been found successful to detect microcalcifications automatically. Among them, Convolutional Neural Networks (CNNs) can automatically learn and extract low-level features that capture contrast and spatial information, and use these features to build robust classifiers. Therefore, spatial enhancement that enhances local contrast based on spatial context is expected to positively influence the learning task of the CNN and, as a result, its classification performance. In this work, we propose a novel spatial enhancement technique for microcalcifications based on the removal of haze, an apparently unrelated phenomenon that causes image degradation due to atmospheric absorption and scattering. We tested the influence of dehazing of digital mammograms on the microcalcification detection performance of two CNNs inspired by the popular AlexNet and VGGnet. Experiments were performed on 1, 066 mammograms acquired with GE Senographe systems. Statistically significantly better microcalcification detection performance was obtained when dehazing was used as preprocessing. Results of dehazing were superior also to those obtained with Contrast Limited Adaptive Histogram Equalization (CLAHE). © 2017, Springer International Publishing AG.
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ItemUOLO - Automatic Object Detection and Segmentation in Biomedical Images( 2018) Teresa Finisterra Araújo ; Guilherme Moreira Aresta ; Adrian Galdran ; Costa,P ; Ana Maria Mendonça ; Aurélio Campilho ; 6825 ; 6321 ; 6320 ; 6381 ; 6071