Abstract: A method of locating anatomical features in a medical imaging dataset comprises obtaining a medical imaging measurement dataset that comprises image data for a subject body as a function of position; and performing a registration procedure that comprises:—providing a mapping between positions in the measurement dataset and positions in a reference dataset, wherein the reference dataset comprises reference image data for a reference body as a function of position, the reference dataset comprises at least one anatomical landmark, and the or each anatomical landmark is indicative of the position of a respective anatomical feature of the reference body; matching image data in the measurement dataset with image data for corresponding positions in the reference dataset, wherein the corresponding positions are determined according to the mapping; determining a measure of the match between the image data of the measurement dataset and the image data of the reference dataset; varying the mapping to improve the match b

Abstract: According to one embodiment there is provided a computer-automated image processing method applied to a four-dimensional (4D) image data set of a patient's abdomen, e.g. by dynamic contrast enhanced computer-assisted tomography (DCE-CT). One of the three-dimensional (3D) scan images is taken to as the reference volume and the others as target volumes. Before registration between the 3D scan images, the image data set is partitioned into an abdominal cavity domain, containing the organs inside the abdominal wall, and an abdominal wall domain including the abdominal wall and externally adjacent skeletal features, such as the spine and ribs. Registration is then carried out separately on the two domains to obtain two warp fields which are then merged into a 4D image data set of the whole volume for further use, which may be to carry out perfusion measurements, to display and to store the registered 4D image data set.

Abstract: Certain embodiments provide a computer system operable to determine a registration mapping between a first medical image and a second medical image, the computer system comprising: a storage device for storing data representing the first medical image and the second medical image; and a processor unit operable to execute machine readable instructions to: (a) identify a plurality of elements in the first medical image; (b) determine a spatial mapping from each element in the first medical image to a corresponding element in the second medical image to provide a plurality of spatial mappings subject to a consistency constraint; and (c) determine a registration mapping between the first medical image and the second medical image based on the plurality of spatial mappings from the respective elements of the first medical image to the corresponding elements of the second medical image.

Abstract: A method and/or system for making determinations regarding samples from biologic sources. A computer implemented method and/or system can be used to automate parts of the analysis.

Abstract: A method of processing image data including performing a first mapping, of a first image dataset to a first reference, performing a second mapping, of a second image dataset to a second reference, and using the first mapping and the second mapping to correlate at least one position in the first image dataset to at least one position in the second image dataset.

Abstract: According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable.

Abstract: According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable.

Abstract: According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable.

Abstract: According to one embodiment there is provided a method of selecting a plurality of M atlases from among a larger group of N candidate atlases to form a multi-atlas data set to be used for computer automated segmentation of novel image data sets to mark objects of interest therein. A set of candidate atlases is used containing a reference image data set and segmentation data. Each of the candidate atlases is segmented against the others in a leave-one-out strategy, in which the candidate atlases are used as training data for each other. For each candidate atlas in turn, the following is carried out: registering; segmenting; computing an overlap; computing a value of the similarity measure for each of the registrations; and obtaining a set of regression parameters by performing a regression with the similarity measure being the independent variable and the overlap being the dependent variable.

Abstract: A polyline tree representation of a coronary artery tree imaged in a volume data set is obtained, and its topology is extracted to give a topological representation indicating the relative positions of vessels in the tree. The topological representation is compared with a set of topological rules to find possible anatomical classifications for each vessel, and a set of candidate labeled polyline trees is generated by labeling the polyline tree with labels showing each combination of possible anatomical classifications.

Abstract: A polyline tree representation of a coronary artery tree imaged in a volume data set is obtained, and its topology is extracted to give a topological representation indicating the relative positions of vessels in the tree. The topological representation is compared with a set of topological rules to find possible anatomical classifications for each vessel, and a set of candidate labeled polyline trees is generated by labeling the polyline tree with labels showing each combination of possible anatomical classifications.

Abstract: A method and/or system for making determinations regarding samples from biologic sources. A computer implemented method and/or system can be used to automate parts of the analysis.

Abstract: A method and/or system for making determinations regarding samples from biologic sources. A computer implemented method and/or system can be used to automate parts of the analysis.

Abstract: A path between specified start and end voxels along a biological object with a lumen, such as a vessel, within a patient image three-dimensional volume data set comprising an array of voxels of varying value is identified using an algorithm that works outwards from the start voxel to identify paths of low cost via intermediate voxels. The intermediate voxels are queued for further expansion of the path using a priority function comprising the sum of the cost of the path already found from the start voxel to the intermediate voxel and the Euclidean distance from the intermediate voxel to the end voxel. A cost function that depends on the voxel density is used to bias the algorithm towards paths inside the object. The number of iterations of the voxel required to find a path from the start to the end voxel, and hence the time taken, can be significantly reduced by scaling the Euclidean distance by a constant. Usefully, the constant is greater than 1, such as between 1.5 and 2.

Abstract: A method for automatically determining a start or finish location near to an end of a lumen in a medical image data set is described. The location may thus be used in determining a camera path for virtual endoscopy (e.g. colonoscopy).

Abstract: An image processing system in which sets of image elements having display values outside of a target range B of display values are each respectively morphologically dilated. The intersection between the morphologically dilated sets of image elements is then identified and those image elements within the intersecting region are removed from the set of image elements having the target range B of display values. This removes image elements incorrectly appearing to have display values corresponding to the target range B of display values due to aliasing effects between regions of image elements having display values either side of the target range B of image values. The imaging may be two-dimensional or three-dimensional imaging. The morphological dilatation is preferably performed with a quasi-circular or a quasi-spherical structuring element having a radius of between two and three voxels.

Abstract: A path between specified start and end voxels along a biological object with a lumen, such as a vessel, within a patient image three-dimensional volume data set comprising an array of voxels of varying value is identified using an algorithm that works outwards from the start voxel to identify paths of low cost via intermediate voxels. The intermediate voxels are queued for further expansion of the path using a priority function comprising the sum of the cost of the path already found from the start voxel to the intermediate voxel and the Euclidean distance from the intermediate voxel to the end voxel. A cost function that depends on the voxel density is used to bias the algorithm towards paths inside the object. The number of iterations of the voxel required to find a path from the start to the end voxel, and hence the time taken, can be significantly reduced by scaling the Euclidean distance by a constant. Usefully, the constant is greater than 1, such as between 1.5 and 2.

Abstract: A method and/or system for making determinations regarding samples from biologic sources including statistical methods for making meaning grouping of observed data and/or for determining an overall quality measure of an assay.

Abstract: A computer automated method that applies supervised pattern recognition to classify whether voxels in a medical image data set correspond to a tissue type of interest is described. The method comprises a user identifying examples of voxels which correspond to the tissue type of interest and examples of voxels which do not. Characterizing parameters, such as voxel value, local averages and local standard deviations of voxel value are then computed for the identified example voxels. From these characterizing parameters, one or more distinguishing parameters are identified. The distinguishing parameter are those parameters having values which depend on whether or not the voxel with which they are associated corresponds to the tissue type of interest. The distinguishing parameters are then computed for other voxels in the medical image data set, and these voxels are classified on the basis of the value of their distinguishing parameters.

Abstract: A computer automated method for setting visualization parameter boundaries in a preset for displaying an image from a 3D data set applicable to magnetic resonance (MR) data, computer tomography (CT) data and other 3D data sets obtained in medical imaging is described. In one example the visualization parameter boundaries are color boundaries. A histogram of data values of voxels within a user-selected volume of interest (VOI) is generated and an analysis of a convex hull spanning the histogram is made to provide one or more visualization thresholds which divide the histogram into sub-regions. The sub-regions relate to different tissue types within the VOI and color boundaries are set based on the visualization thresholds for displaying the different tissue types in different colors. The method allows color boundaries in a preset to be set objectively and automatically so that images can be displayed consistently and with less user manipulation.