Toshiba Medical Systems Corporation

Kabushiki Kaisha Toshiba

Costas Plakas

Ian Poole

Jim Piper

Saad Masood

Brian Mohr

Marco Razeto

Shaobin Wang

Akinola Akinyemi

Lu Bai

Qi Chen

Yanli Wang

Jing Dai

Yanhua Wang

Minfeng Xu

Jun Wan

Jianchun Zhao

An apparatus for processing volumetric image data to identify vessel regions having a predetermined condition, comprises a measurement unit for measuring at least one vessel parameter, a reference identification unit for obtaining data representing a branch-free vessel and identifying at least one reference section of the branch-free vessel, a calculation unit for calculating an expected value of the parameter in a further section of the branch-free vessel based on at least one measured value of the parameter in the at least one reference section, and a region identification unit for identifying a region of the vessel having the predetermined condition in dependence on both the expected value of the parameter in the further section and a measured value of the parameter in the further section.

Method of, and apparatus for, processing volumetric image data

An apparatus for processing multi-energy image data to separate at least two types of material comprises a classification unit, wherein the classification unit is configured to obtain a classification of pixels or voxels belonging to the types of material based on a threshold which is adaptively changed in dependence on multi-energy intensity information associated with the pixels or voxels.

Method of, and apparatus for, material classification in multi-energy image data

A medical image processing apparatus according to one embodiment includes obtaining circuitry, calculating circuitry, and transforming circuitry. The obtaining circuitry takes an expiration time phase or an inspiration time phase as a benchmark time phase and obtains a benchmark line structure, which is a line structure of a bronchus in a lung field, from a medical image at the benchmark time phase. The calculating circuitry calculates a motion amount between a component depicted in the medical image at the benchmark time phase and a component depicted in a medical image at at least one other time phase than the benchmark time phase. The transforming circuitry transforms the benchmark line structure based on the motion amount to obtain an estimated line structure, which is estimated as a line structure at the at least one other time phase.

Medical image processing apparatus and medical image processing method

A medical imaging data processing apparatus comprises a setting circuitry configured to set a plurality of seeds at different locations in medical image data, a processing circuitry configured to select at least one seed from among the plurality of seeds and expand the at least one selected seed, and a region identifying circuitry configured to identify at least one target region based on a result of the expansion.

Medical imaging data processing apparatus and method

A computer implemented method identifying calcification in a patient image data set including blood vessels. The method includes: obtaining an image data set including voxels each having an intensity value; forming the intensity values into an intensity value group covering an intensity range; defining plural intensity thresholds across the intensity range and including its end values; for each intensity threshold, partitioning the intensity values into two sub-groups according to intensity threshold, and calculating an information criterion based on intensity threshold; generating an information criterion measure curve that plots the calculated information criteria against intensity threshold; locating a maximum in the information criterion measure curve and setting the corresponding intensity threshold as a calcification threshold; and partitioning the intensity values into two sub-groups using the calcification threshold, identifying voxels corresponding to intensity values in the sub-group above the calcification threshold as representing calcification in the patient.

Method and system for identification of calcification in imaged blood vessels

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:&#x2014;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 between the image data of the measurement dataset and the image data of the reference dataset, thereby to obtain a registration mapping; and using the registration mapping to map the positions of the anatomical landmarks to positions in the measurement dataset, thereby to assign positions to anatomical features in the measurement dataset.

Feature location method and system

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 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

Costas Plakas