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S.P. Morrissey

Bio: S.P. Morrissey is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Image registration & Medical imaging. The author has an hindex of 1, co-authored 1 publications receiving 16 citations.

Papers
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Proceedings ArticleDOI
12 Apr 2007
TL;DR: A new optimisation algorithm is introduced (called NEWUOA) to address the above registration problems, and its robustness and accuracy properties are demonstrated.
Abstract: A number of problems frequently encountered in brain image analysis can be conveniently solved within a registration framework, such as alignment of mono- or multi-sequence magnetic resonance images (MRI) for single or multiple subjects, computation of the cerebral mid-sagittal plane in anatomical or diffusion-tensor MRI, correction of acquisition distortions in diffusion-weighted MRI, etc. A widely used approach for registration tasks consists of maximising a similarity criterion between the intensities of the images to be matched. In this context, efficient optimisation methods are needed to obtain good performances. In this paper, we introduce a new optimisation algorithm (called NEWUOA) to address the above registration problems, and we demonstrate its robustness and accuracy properties

16 citations


Cited by
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Book ChapterDOI
29 Oct 2007
TL;DR: The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation.
Abstract: Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed in this paper are based on the Non-Local (NL) means algorithm. This approach uses the natural redundancy of information in images to remove the noise. We introduce three variations of the NL-means algorithms adapted to DW-MRI and to DT-MRI. Experiments were carried out on a set of 12 diffusion-weighted images (DW-MRI) of the same subject. The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation.

88 citations

01 Jan 2005
TL;DR: P pH-mediated control over the mesostructure of ordered mesoporous materials templated by polyion complex micelles, Beilstein Journal of Nanotechnology 2019, 10, 144-156.
Abstract: Albane Birault, Emilie Molina, Philippe Trens, Didier Cot, Guillaume Toquer, Nathalie Marcotte, Carole Carcel, John R. Bartlett, Corine Gerardin and Michel Wong Chi Man Periodic Mesoporous Organosilicas from Polyion Complex Micelles Effect of Organic Bridge on Nanostructure, Eur. J. Inorg. Chem. 20193157-3164 Didier Tichit, Geraldine Layrac and Corine Gérardin Synthesis of layered double hydroxides through continuous flow processes: A review, Chem. Eng. J. 2019, 369, 302-332 Albane Birault, Emilie Molina, Carole Carcel, John Bartlett, Nathalie Marcotte, Guillaume Toquer, Patrick Lacroix-Desmazes, Corine Gérardin and Michel Wong Chi Man Synthesis of lamellar mesostructured phenylene-bridged periodic mesoporous organosilicas (PMO) templated by polyion complex (PIC) micelles, J. Sol-Gel Sci. Technol. 2019, 89, 189-195 Emilie Molina, Mélody Mathonnat, Jason Richard, Patrick Lacroix-Desmazes, Martin In, Philippe Dieudonne, Thomas Cacciaguerra, Corine Gérardin and Nathalie Marcotte pH-mediated control over the mesostructure of ordered mesoporous materials templated by polyion complex micelles, Beilstein Journal of Nanotechnology 2019, 10, 144-156

50 citations

Journal ArticleDOI
TL;DR: This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines, Gaussian elastic body splines (GEBS), or cubic B-splines.
Abstract: Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function.

38 citations

Journal ArticleDOI
TL;DR: Questions are raised about the functional role of these MRI tracks and their relation with laterality about the short association fibers connecting the pre- and postcentral gyri.
Abstract: Introduction Diffusion tensor imaging and tractography allow studying white matter fiber bundles in the human brain in vivo. Electrophysiological studies and postmortem dissections permit improving our knowledge about the short association fibers connecting the pre- and postcentral gyri. The aim of this study was first to extract and analyze the features of these short fiber bundles and secondly to analyze their asymmetry according to the subjects' handedness. Methods Ten right-handed and ten left-handed healthy subjects were included. White matter fiber bundles were extracted using a streamline tractography approach, with two seed regions of interest (ROI) taken from a parcellation of the pre- and postcentral gyri. This parcellation was achieved using T1 magnetic resonance images (MRI) and semi-automatically generated three ROIs within each gyrus. MRI tracks were reconstructed between all pairs of ROIs connecting the adjacent pre- and postcentral gyri. A quantitative analysis was performed on the number of tracks connecting each ROI pair. A statistical analysis studied the repartition of these MRI tracks in the right and left hemispheres and as a function of the subjects' handedness. Results The quantitative analysis showed an increased density of MRI tracks in the middle part of the central area in each hemisphere of the 20 subjects. The statistical analysis showed significantly more MRI tracks for the left hemisphere, when we consider the whole population, and this difference was presumably driven by the

21 citations

Journal ArticleDOI
TL;DR: An interactive system for the semantic annotation of brain magnetic resonance images that uses both a numerical atlas and symbolic knowledge of brain anatomical structures depicted using the semantic Web standards to produce annotations with high precision and quality.
Abstract: This paper describes an interactive system for the semantic annotation of brain magnetic resonance images. The system uses both a numerical atlas and symbolic knowledge of brain anatomical structures depicted using the semantic Web standards. This knowledge is combined with graphical data, automatically extracted from the images by imaging tools. The annotations of parts of gyri and sulci, in a region of interest, rely on constraint satisfaction problem solving and description logics inferences. The system is run on a client-server architecture, using Web services and including a sophisticated visualization tool. An evaluation of the system was done using normal (healthy) and pathological cases. The results obtained so far demonstrate that the system produces annotations with high precision and quality.

19 citations