Nathan Faggian

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The corpus callosum is the largest white matter tract, important for interhemispheric communication. The aim of this study was to investigate and compare corpus callosum size, shape and diffusion characteristics in 106 very preterm infants and 22 full-term infants. Structural and diffusion magnetic resonance images were obtained at term equivalent. The(More)
The hippocampus undergoes rapid growth and development in the perinatal months. Infants born very preterm (VPT) are vulnerable to hippocampal alterations, and can provide a model of disturbed early hippocampal development. Hippocampal shape alterations have previously been associated with memory impairment, but have never been investigated in infants. The(More)
The aim of this study was to relate altered corpus callosum (CC) integrity in 106 very preterm (VPT) infants (<30 weeks' gestational age or <1250 g birth weight) at term equivalent to perinatal predictors and neurodevelopmental outcomes at two years. T1 and diffusion magnetic resonance images were obtained. The CC was traced, and divided into six(More)
The neurobiological processes resulting in epilepsy, known as epileptogenesis, are incompletely understood. Manganese-enhanced MRI (MEMRI) can potentially aide in this quest as it provides superior tissue contrast, particularly of the hippocampal subregions. This longitudinal study aims to characterise the changes in the hippocampus of the post kainic(More)
In this paper, a novel approach to impulsive noise detection with automatic parameter tuning is proposed for colour image restoration. First, a simple noise estimator is used to estimate the amount of noise in the given image, then a global adaptive region growing scheme (ARG) is used to separate uncorrupted clusters of pixels from the corrupted clusters of(More)
Face recognition from video can be improved if good face segmentation of the subject under test is achieved. Many video based face recognition rely on simple background modeling and coarse alignment strategies for segmentation. This work presents a face recognition from video framework based on using Active Appearance Models (AAM) to achieve accurate face(More)
Active Appearance Models (or AAMs) are fast linear models for appearance variation in images. A key disadvantage of AAMs is the requirement for hand-labeled correspondence points. We use Morphable Model (or MM) data to avoid hand-labeling error and test the convergence performance of a well known fitting method, Inverse Compositional Image Alignment (or(More)
This paper presents a fast fitting method for 3D Morphable Models (3DMMs). In most cases fitting a Morphable Model to an image is done using slow non-linear optimization processes. We avoid this by introducing a relationship to Active Appearance Models (AAMs) that can be used to linearize the non-linear optimization problem of 3DMM fitting. Using the(More)