Abstract: Techniques of storing and retrieving medical data and information are provided. Medical result information can be stored on a long-term data repository in a shared network, e.g., the Internet. Pseudonymized identifiers of patients can be used to retrieve such data and information.

Abstract: At least one example embodiment relates to a computer-implemented method comprising receiving a medical image dataset; receiving at least one medical comparison image dataset; extracting biometric data based on the medical image dataset; extracting biometric comparison data based on the medical comparison image dataset; determining a measure of difference between the biometric comparison data and the biometric data; and assigning the medical image dataset to the medical comparison image dataset when the measure of difference does not exceed a threshold value.

Abstract: In a method and apparatus for providing a visualization image and a reference image for a patient, with only one reconstruction being implemented, raw scan data from the patient are reconstructed into a visualization image, and the reference image is generated, without another reconstruction, by applying a filter to the visualization image to obtain a simulated reference image, using a value of a filter parameter that is specific for the patient. The patient-specific filter parameter is determined by comparing image-derived properties of an accessed, previously-reconstructed reference image and the reference image, and the filter parameter is adapted to reduce differences in the image-derived properties of the respective images.

Abstract: In a method and apparatus for automatic background region selection for lesion segmentation in medical images, a patient medical image dataset is loaded into a computer and an image region is delineated to obtain a segmentation containing a lesion. A background region is created from the segmentation representing the patient organ.

Abstract: In a method and apparatus for generating an enhanced image for display from medical imaging data of a subject, a feature of interest in the imaging data elongated in at least one dimension is determined. A location of a line through the imaging data along the feature of interest is obtained, and a projection image along the line is generated from the imaging data.

Abstract: In a method for improving alignment between representations of a same elongate feature in first and second images, the images are provided to a processor and the processor derives a centerline from image data representing an elongate anatomical feature in the first image, and derives a value of a metric indicating a present quality of alignment of the centerline with image data of the second image representing the same anatomic feature. The processor optimizes the metric to provide a rigid transform, and applies the rigid transform to the centerline or the second image to improve the alignment between the first and the second image with respect to the elongate feature.

Abstract: In a method and apparatus for automatic background region selection for lesion segmentation in medical images, a patient medical image dataset is loaded into a computer and an image region is delineated to obtain a segmentation containing a lesion. A background region is created from the segmentation representing the patient organ.

Abstract: In a method and an apparatus to visualize nuclear medicine data from different modalities in a single image, image data sets acquired with different modalities are aligned with a data set representing corresponding anatomical data. The image data in each data set are segmented into separate regions, representing respective structures of interest, with reference to a segmentation derived from anatomical data. For each region, a corresponding segment of image data is selected from a selected image data set. The selected segments of the image data set are combined to generate a multi-fused image of the regions, by applying spatially dependent look-up tables to the multiple image data sets, thereby to determine whether each data set is hidden or displayed in each region.

Abstract: A method of calibrating image data from a given medical imaging protocol, comprising: obtaining a visualization image by reconstruction of raw scan data 22 using a reconstruction 24 and applying a filter 30 to the visualization image, to generate a simulated reference image 32. The parameters of the filter 30 are determined from earlier comparisons of simulated reference image 32 with reference images 28 reconstructed from the corresponding raw scan data set by a standard reference standard reference reconstruction 24?.

Abstract: A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.

Abstract: In a method for improving alignment between representations of a same elongate feature in first and second images, the images are provided to a processor and the processor derives a centerline from image data representing an elongate anatomical feature in the first image, and derives a value of a metric indicating a present quality of alignment of the centerline with image data of the second image representing the same anatomic feature. The processor optimizes the metric to provide a rigid transform, and applies the rigid transform to the centerline or the second image to improve the alignment between the first and the second image with respect to the elongate feature.

Abstract: In a method for calculating and displaying a summary of changes in image-derived measurements of one or more properties of one or more regions of interest whereby a representation is presented to a user indicating a change in the property of at least one of the regions-of-interest, with an indication of the region-of-interest associated with the representation.

Abstract: In a method and an apparatus to visualize nuclear medicine data from different modalities in a single image, image data sets acquired with different modalities are aligned with a data set representing corresponding anatomical data. The image data in each data set are segmented into separate regions, representing respective structures of interest, with reference to a segmentation derived from anatomical data. For each region, a corresponding segment of image data is selected from a selected image data set. The selected segments of the image data set are combined to generate a multi-fused image of the regions, by applying spatially dependent look-up tables to the multiple image data sets, thereby to determine whether each data set is hidden or displayed in each region.

Abstract: In a method and system for facilitating user-interactive navigation of medical image data, a set of medical imaging data of a subject is obtained and, from the medical imaging data an image volume reviewable by a user is generated. From the imaging data, a navigation map is generated that is a user-interactive image that shows the image volume. The navigation map is displayed alongside an image representing a region of the image volume. A selected part of the image volume is identified for review in response to a user selection of a location on the navigation map corresponding to the part.

Abstract: In a method and apparatus for generating an enhanced image for display from medical imaging data of a subject, a feature of interest in the imaging data elongated in at least one dimension is determined. A location of a line through the imaging data along the feature of interest is obtained, and a projection image along the line is generated from the imaging data.

Abstract: A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.

Abstract: A system and method for tree matching are provided. The method for tree matching includes; acquiring tree-like structures representing a physical object or model; extracting a path from a first tree-like structure and a path from a second tree-like structure; comparing the paths of the first and second tree-like structures by computing a similarity measurement for the paths; and determining if the paths match based on the similarity measurement.