Kabushiki Kaisha Toshiba

Toshiba Medical Systems Corporation

Jim Piper

Ian Poole

Costas Plakas

Akinola Akinyemi

Lu Bai

Jing Dai

Brian Mohr

Marco Razeto

Jun Wan

Sean Daniel Murphy

Shaobin Wang

Jian Song

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.

Image processing method and apparatus

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.

Image registration

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.

Image processing apparatus and method

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.

Processing of abdominal images

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.

Image segmentation

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. The M atlases are then selected from among all the N candidate atlases to form the multi-atlas data set, the M atlases being those atlases determined to collectively provide the highest aggregate overlap over all the training data image sets.

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Edinburgh, United Kingdom

Jim Piper