Optimization of Data Transfer for Grid Using GridFTP

Grid is a highly distributed heterogeneous environment which interoperates towards completing a common goal. As such, grids performance is highly dependant on the speed of data transfer between systems of which grid consists. Most commonly used and a “de facto” standard for data transfer on grid systems is GridFTP [7]. While working on CRO-GRID Infrastructure [3] project, we faced problems with small data transfer speeds when transferring small files. In this paper we present a program we have developed which uses GridFTP and speeds up data transfer of large number of small or medium sized files. We also present empirical results of the performance measurements achieved while using the implemented system

Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis

A novel statistical model based on texture and shape for fully automatic intraretinal layer segmentation of normal retinal tomograms obtained by a commercial 800nm optical coherence tomography (OCT) system is developed. While existing algorithms often fail dramatically due to strong speckle noise, non-optimal imaging conditions, shadows and other artefacts, the novel algorithm’s accuracy only slowly deteriorates when progressively increasing segmentation task difficulty. Evaluation against a large set of manual segmentations shows unprecedented robustness, even in the presence of additional strong speckle noise, with dynamic range tested down to 12dB, enabling segmentation of almost all intraretinal layers in cases previously inaccessible to the existing algorithms. For the first time, an error measure is computed from a large, representative manually segmented data set (466 B-scans from 17 eyes, segmented twice by different operators) and compared to the automatic segmentation with a difference of only 2.6% against the inter-observer variability

Non-invasive detection of early retinal neuronal degeneration by ultrahigh resolution optical coherence tomography.

Optical coherence tomography (OCT) has revolutionises the diagnosis of retinal disease based on the detection of microscopic rather than subcellular changes in retinal anatomy. However, currently the technique is limited to the detection of microscopic rather than subcellular changes in retinal anatomy. However, coherence based imaging is extremely sensitive to both changes in optical contrast and cellular events at the micrometer scale, and can generate subtle changes in the spectral content of the OCT image. Here we test the hypothesis that OCT image speckle (image texture) contains information regarding otherwise unresolvable features such as organelle changes arising in the early stages of neuronal degeneration. Using ultrahigh resolution (UHR) OCT imaging at 800 nm (spectral width 140 nm) we developed a robust method of OCT image analyses, based on spatial wavelet and texture-based parameterisation of the image speckle pattern. For the first time we show that this approach allows the non-invasive detection and quantification of early apoptotic changes in neurons within 30 min of neuronal trauma sufficient to result in apoptosis. We show a positive correlation between immunofluorescent labelling of mitochondria (a potential source of changes in cellular optical contrast) with changes in the texture of the OCT images of cultured neurons. Moreover, similar changes in optical contrast were also seen in the retinal ganglion cell- inner plexiform layer in retinal explants following optic nerve transection. The optical clarity of the explants was maintained throughout in the absence of histologically detectable change. Our data suggest that UHR OCT can be used for the non-invasive quantitative assessment of neuronal health, with a particular application to the assessment of early retinal disease

Media 15: Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

Originally published in Biomedical Optics Express on 01 January 2013 (boe-4-1-134

Media 5: Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

Originally published in Biomedical Optics Express on 01 January 2013 (boe-4-1-134

Media 14: Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

Originally published in Biomedical Optics Express on 01 January 2013 (boe-4-1-134

Media 1: Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

Originally published in Biomedical Optics Express on 01 January 2013 (boe-4-1-134

Media 10: Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data

Originally published in Biomedical Optics Express on 01 January 2013 (boe-4-1-134