Abstract: A method of selecting image data representative of a subject from an image data set comprises determining regions of image data, wherein each region of image data consists of a respective plurality of connected voxels, and selecting at least one region as being representative of the subject based upon at least one of the size and shape of the region.

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: Certain embodiments provide a computer apparatus operable to carry out a data processing method to stitch together overlapping three-dimensional image data sets to form a joint data set.

Abstract: An image processing apparatus may include: a first registration device for performing, by taking a first input image of two overlapped input images having an overlapped area as a reference image, a first registration on a second input image to find, in the second input image, a second pixel which is matched with each first pixel located in the overlapped area of the reference image; an output pixel location determination device for calculating a location of an output pixel which is located in the overlapped area of the output image and corresponds to the first pixel, the locations of the first and second pixels being respectively weighted, and the shorter the distance from the first pixel to a non-overlapped area of the reference image is, the greater a weight of the location of the first pixel is; and an output pixel value determination device for calculating a pixel value.

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: 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: An image processing apparatus may include: a first registration device for performing, by taking a first input image of two overlapped input images having an overlapped as a reference image, a first registration on a second input image, to find, in the second input image, a second pixel which is matched with each first pixel located in the overlapped area of the reference image; an output pixel location determination device for calculating, a location of an output pixel which is located in the overlapped area of the output image and corresponds to the first pixel, wherein the locations of the first and second pixels are respectively weighted, and the shorter the distance from the first pixel to a non-overlapped area of the reference image is, the greater a weight of the location of the first pixel is; and an output pixel value determination device for calculating a pixel value.

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: 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: An apparatus (10) for detecting a particle (12) labelled with a fluorescent marker is disclosed. The apparatus (10) has a flow cell (16) being adapted to contain a fluid (14) in which the particle (12) is suspended. A light source (28) is operatively coupled to the flow cell (16) and arranged for emitting a stimulating light (28) which is effective in optically exciting the fluorescent marker (12) for emitting a fluorescent light (30). The apparatus (10) also includes a spatial filter (50) across an optical path between the particle (12) and a time gated detector (32) operatively coupled to the flow cell (16) for detecting the fluorescent light (30).