Abstract: Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.

Abstract: Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of the training data into clusters at their leaves. In embodiments probability distributions representing the clusters at each tree are aggregated to form a forest density which is an estimate of a probability density function from which the unlabeled data may be generated. A mapping engine may use the clusters at the leaves of the density forest to estimate a mapping function which maps the unlabeled data to a lower dimensional space whilst preserving relative distances or other relationships between the unlabeled data points. A sampling engine may use the density forest to randomly sample data from the forest density.

Abstract: Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of the training data into clusters at their leaves. In embodiments probability distributions representing the clusters at each tree are aggregated to form a forest density which is an estimate of a probability density function from which the unlabeled data may be generated. A mapping engine may use the clusters at the leaves of the density forest to estimate a mapping function which maps the unlabeled data to a lower dimensional space whilst preserving relative distances or other relationships between the unlabeled data points. A sampling engine may use the density forest to randomly sample data from the forest density.

Abstract: Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.

Abstract: The present discussion relates to automated image data processing and visualization. One example can facilitate generating a graphical user-interface (GUI) from image data that includes multiple semantically-labeled user-selectable anatomical structures. This example can receive a user selection of an individual semantically-labeled user-selectable anatomical structure. The example can locate a sub-set of the image data associated with the individual semantically-labeled user-selectable anatomical structure and can cause presentation of the sub-set of the image data on a subsequent GUI.

Abstract: A method for detecting and identifying structures of interest such as colonic polyps or similar structures like lung nodules in volumetric (medical) images data is provided. The method includes obtaining a heat diffusion field (HDF) by applying a heat diffusion scheme to a volume of interest that includes structures. The obtained heat diffusion field is then used for identifying a structure of interest from the structures in the volume of interest using a geometrical analysis of the heat diffusion field. The heat diffusion scheme is, at least partly, governed by non-linear diffusion parameters. The identification includes two parts: (i) the computation of a spherical symmetry parameter, and (ii) the performance of a local analysis of the volume of interest and computation of a triangulization parameter.

Abstract: A method for detecting and identifying structures of interest such as colonic polyps or similar structures like lung nodules in volumetric (medical) images data is provided. The method includes obtaining a heat diffusion field (HDF) by applying a heat diffusion scheme to a volume of interest that includes structures. The obtained heat diffusion field is then used for identifying a structure of interest from the structures in the volume of interest using a geometrical analysis of the heat diffusion field. The heat diffusion scheme is, at least partly, governed by non-linear diffusion parameters. The identification includes two parts: (i) the computation of a spherical symmetry parameter, and (ii) the performance of a local analysis of the volume of interest and computation of a triangulization parameter.