ICA-based sparse feature recovery from fMRI datasets

Spatial Independent Components Analysis (ICA) is increasingly used in the context of functional Magnetic Resonance Imaging (fMRI) to study cognition and brain pathologies. Salient features present in some of the extracted Independent Components (ICs) can be interpreted as brain networks, but the segmentation of the corresponding regions from ICs is still ill-controlled. Here we propose a new ICA-based procedure for extraction of sparse features from fMRI datasets. Specifically, we introduce a new thresholding procedure that controls the deviation from isotropy in the ICA mixing model. Unlike current heuristics, our procedure guarantees an exact, possibly conservative, level of specificity in feature detection. We evaluate the sensitivity and specificity of the method on synthetic and fMRI data and show that it outperforms state-of-the-art approaches

A geometric alternative to computed tomography

This paper describes parts of a patent taken out by the INRIAThis paper describes a totally new way to process Xray data in order to reconstruct the external and internal boudaries of objects, which do not involve Computed Tomography (CT). We show that the segmentation can be performed directrly with the raw data, the sinogram produced with the scanner, and that those segmented shapes can be geomtetrically transformed into reconstructed shapes in the usual space. Thus, if we are interested in only the boundaries of the objects, our method eliminates the computationally expensive step of Computed Tomography. Experimental results are presented for both synthetic and real data, leading to subpixel positioning fo the reconstructed boundaries. Our method gives its best results for sparse, high contrasted objects such as bones or blood vessels in angiograms. It can be adapted to any kind of scanner, including 3D scabbers. At last, we present an extension of our method which allows "on the fly" processing of the data and real time tracking of the objects boudnaries

New feature points based on geometric invariants for 3D image registration

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The Gradient and laplacien filtered back projection operators

This paper presents a new method, based on the Filtered Back Projection technique (FBP) to compute directly the values of the gradient and the laplacien of an X-Ray image. We propose a way to pre-process the raw data that allow us to compute directly the reconstructed values of the gradient or of the laplacien at any location in the plane (defined with real coordinates) without reconstructing an image of the absorption coefficients. The reconstructed values of the gradient and of the laplacien correspond to the exact mathematical definition of the differentials of the image and do not imply the use of a band limited filter depending on a constant s, as proposed previously by other authors. For noisy data, we propose also an extension of existing FBP techniques, adapted to the computation of the gradient and of the laplacien. At last, we show how to use those new algorithms to perform the segmentation of a slice, without image reconstruction. Images of the reconstructed gradient, laplacien and segmented objects are presented

Realistic 3D simulation of shapes and shadows for image processing

This paper illustrates the cooperation between Image Processing and Computer Graphics. We present a new method to compute realistic 3D images of building or complex objects from a set of real pictures and from the 3D model of a real scene. We also show how to remove shadows from those pictures and how to simulate new lightnings. Our system allows the generation of synthetic pictures, with a total control over the position of the camera, over the features of the optical system, and over the solar lightning. We propose several methods to avoid most of the artefact which would be produced by a straghtforward application of our approach. At last, we propose a general scheme to use these pictures in order to design new optical systems and to test Image Processing algorithms, long before the building of the first physical prototype

Improving accuracy and power with transfer learning using a meta-analytic database

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e. to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.Comment: MICCAI, Nice : France (2012

Deformation Analysis to Detect and Quantify Active Lesions in Three-Dimensional Medical Image Sequences

International audienceAbstract--Evaluating precisely the temporal variations of lesion volumes is very important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery, and patient follow-up. In this paper we present a volumetric analysis technique, combining precise rigid registration of three-dimensional (3-D) (volumetric) medical images, nonrigid deformation computation, and flow-field analysis. Our analysis technique has two outcomes: the detection of evolving lesions and the quantitative measurement of volume variations. The originality of our approach is that no precise segmentation of the lesion is needed but the approximative designation of a region of interest (ROI) which can be automated. We distinguish between tissue transformation (image intensity changes without deformation) and expansion or contraction effects reflecting a change of mass within the tissue. A real lesion is generally the combination of both effects. The method is tested with synthesized volumetric image sequences and applied, in a first attempt to quantify in vivo a mass effect, to the analysis of a real patient case with multiple sclerosis (MS)

Calcul de variations de volume de lésions dans des images médicales tridimensionnelles

L'évaluation précise de la variation temporelle du volume d'une lésion est très importante pour diverses applications : recherche pharmaceutique et médicale, suivi des patients, prise de décision thérapeutique. Dans cet article, nous décrivons un modèle de croissance de lésions, qui nous sert à valider une technique originale de mesure de variation de volume. Dans un premier temps nous effectuons un recalage rigide des images, ensuite nous calculons le champ des déformations résiduelles que nous intégrons sur des surfaces emboîtées épousant la forme de la structure évolutive. Le maximum du profil obtenu correspond à la variation de volume cherchée dans le cas synthétique et conduit à des valeurs cohérentes avec l'intuition dans le cas d'un patient atteint de sclérose en plaques

Image surface extremal points, new feature points for image registration

We generalize the definition of the extremal points (EP) to image surfaces and also to hyper-surfaces in any dimensions. These feature points can be used for image analysis and image registration because their relative positions are invariant with respect to rigid transforms. In a previous paper, we have defined the extremal points of the object surface for 3D images, and we have shown how to extract those points and how to use them to perform automatically the accurate registration of 3D medical images. The extremal points of the object surface are also invariant with changes of the image dynamic, because they are intrinsic to the object surface. We show now that another kind of extremal points can be defined in 3D, from the 4D image surface (x, y, z, f(x, y, z)). We explain how to compute and extract those new extremal points and then present registration experiments, comparing the results between the use of the extremal points of the object surface and of the image surface. We conclude by showing that both methods have their own advantages, leading to the extraction of extremely precise feature points and to the reliable registration of 3D images

Multiscale Extraction and Representation of Features from Medical Images

For automatic registration of medical images, we must search for geometric features that are invariant both with respect to rigid transformations and to smooth changes of resolution. Beginning with Witkin's seminal paper, scale space theory provides an elegant framework for studying the multiscale behavior of these characteristics. However, a natural scale-space representation of features, useful for practical applications, is still missing. We address here the problem of multiscale extraction and representation of characteristic points based on iso-surface techniques. Our main concern is with $2D$ images: we analyze corner points at increasing scales using the Marching Lines algorithm. Since we can exploit the intrinsic nature of intensity of medical images, segmentation of components or parameterization of curves is not needed, in contrast with other methods. Due to the direct use of the coordinates of points, we get a representation of orbits, which is very convenient both for detection at coarse scale and for localization at fine scale. We find that the significance of \corner\ depends not only on their scale-space lifetime but also on their relationship with curvature inflexion points