Abstract: A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.

Abstract: The present invention is related to a method for the authentication of an article comprising the steps of generating identification data about an article, geometrical coding the identification data to form one geometric coding, incorporating the geometrical coding into a random pattern to forman authentication pattern, and embedding physically the authentication pattern onto the surface of the article to create a specific roughness.

Abstract: A method and system for acquiring information on lesions in dynamic 3D medical images of a body region and/or organ, having the steps: applying a registration technique to align a plurality of volumetric image data of the body region and/or organ, yielding multi-phase registered volumetric image data of the body region and/or organ; applying a hierarchical segmentation on the multi-phase registered volumetric image data of the body region and/or organ, the segmentation yielding a plurality of clusters of n-dimensional voxel vectors of the multi-phase aligned volumetric image; determining from the plurality of clusters a cluster or set of clusters delineating the body and/or organ; identifying the connected region(s) of voxel vectors belonging to the body region and/or organ; refining/filling the connected region(s) corresponding to the body region and/or organ; reapplying the segmentation step to the refined/filled connected region(s) corresponding to the body region and/or organ, to obtain a more accurate se

Abstract: The present invention relates to a method for processing and presenting at least a first image (102) and a second image (103), these images being digital medical images. A first step in the method is performing image registration between the first image (102) and the second image (103) to generate a pixel-level mapping between both images. The registration takes into account a region-level correspondence between the first image (102) and the second image (103) A second step is presenting the first image (102) and the second image (103) simultaneously on a display (103). A third step is presenting a first magnification (104) of a region of interest (106) in the first image (102) and a second magnification (105) of a corresponding region (108) in the second image (103). The corresponding region (108) in the second image (103) being determined based on the pixel-level mapping.

Abstract: A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.