Tom MacGillivray

Bio: Tom MacGillivray is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Medicine & Magnetic resonance imaging. The author has an hindex of 36, co-authored 160 publications receiving 5177 citations. Previous affiliations of Tom MacGillivray include Harvard University & Edinburgh Napier University.

Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures

Abstract: The retinal and cerebral microvasculatures share many morphological and physiological properties. Assessment of the cerebral microvasculature requires highly specialized and expensive techniques. The potential for using non-invasive clinical assessment of the retinal microvasculature as a marker of the state of the cerebrovasculature offers clear advantages, owing to the ease with which the retinal vasculature can be directly visualized in vivo and photographed due to its essential two-dimensional nature. The use of retinal digital image analysis is becoming increasingly common, and offers new techniques to analyse different aspects of retinal vascular topography, including retinal vascular widths, geometrical attributes at vessel bifurcations and vessel tracking. Being predominantly automated and objective, these techniques offer an exciting opportunity to study the potential to identify retinal microvascular abnormalities as markers of cerebrovascular pathology. In this review, we describe the anatomical and physiological homology between the retinal and cerebral microvasculatures. We review the evidence that retinal microvascular changes occur in cerebrovascular disease and review current retinal image analysis tools that may allow us to use different aspects of the retinal microvasculature as potential markers for the state of the cerebral microvasculature.

Structural disconnectivity in schizophrenia: a diffusion tensor magnetic resonance imaging study.

Abstract: Background There is growing evidence that schizophrenia is a disorder of cortical connectivity. Specifically, frontotemporal and frontoparietal connections are thought to be functionally impaired. Diffusion tensor magnetic resonance imaging (DT—MRI) is a technique that has the potential to demonstrate structural disconnectivity in schizophrenia. Aims To investigate the structural integrity of frontotemporal and frontoparietal white matter tracts in schizophrenia. Method Thirty patients with DSM—IV schizophrenia and thirty matched control subjects underwent DT—MRI and structural MRI. Fractional anisotropy — an index of the integrity of white matter tracts — was determined in the uncinate fasciculus, the anterior cingulum and the arcuate fasciculus and analysed using voxel-based morphometry. Results There was reduced fractional anisotropy in the left uncinate fasciculus and left arcuate fasciculus in patients with schizophrenia compared with controls. Conclusions The findings of reduced white matter tract integrity in the left uncinate fasciculus and left arcuate fasciculus suggest that there is frontotemporal and frontoparietal structural disconnectivity in schizophrenia.

Human cognitive abilities: A survey of factor analytic studies

Abstract: (1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.