Abstract: A computer implemented method of determining a dimension of a gap between an edge of an aerofoil and a surface of an engine casing, the method comprising: receiving data; generating a three dimensional model of the surface of the engine casing from the received data; identifying the edge of the aerofoil in the received data; determining a three dimensional position of a first location along the edge of the aerofoil in the received data using the identified edge; and determining a distance between the determined three dimensional position of the first location and the three dimensional model of the surface of the engine casing using an algorithm.

Abstract: A medical imaging data processing apparatus comprises a receiving unit configured to receive first medical imaging data that represents a region of a subject at a first time, and second medical imaging data that represents the region of the subject at a second, later time, a registration unit configured to perform a registration procedure to obtain registration data representative of a registration between the first medical imaging data and the second medical imaging data, an evaluation unit configured to process the registration data to determine, for each of a plurality of positions in the region, a value of a registration measure, the registration measure representing at least one feature of the registration as a function of position, wherein the evaluation unit is configured to, for at least some of the plurality of positions in the region, determine whether an abnormality is present by comparing the value of the registration measure to a statistical atlas.

Abstract: A computer implemented method of determining a dimension of a gap between an edge of an aerofoil and a surface of an engine casing, the method comprising: receiving data; generating a three dimensional model of the surface of the engine casing from the received data; identifying the edge of the aerofoil in the received data; determining a three dimensional position of a first location along the edge of the aerofoil in the received data using the identified edge; and determining a distance between the determined three dimensional position of the first location and the three dimensional model of the surface of the engine casing using an algorithm.

Abstract: A medical imaging data processing apparatus comprises a receiving unit configured to receive first medical imaging data that represents a region of a subject at a first time, and second medical imaging data that represents the region of the subject at a second, later time, a registration unit configured to perform a registration procedure to obtain registration data representative of a registration between the first medical imaging data and the second medical imaging data, an evaluation unit configured to process the registration data to determine, for each of a plurality of positions in the region, a value of a registration measure, the registration measure representing at least one feature of the registration as a function of position, wherein the evaluation unit is configured to, for at least some of the plurality of positions in the region, determine whether an abnormality is present by comparing the value of the registration measure to a statistical atlas.

Abstract: Certain embodiments provide a computer apparatus operable to carry out a data processing method to position a set of anatomical landmarks in a three-dimensional image data set of a part or all of a patient, comprising: providing a trained supervised machine learning algorithm which has been trained to place each of the set of anatomical landmarks; applying the supervised machine learning algorithm to place the set of anatomical landmarks relative to the data set; providing a trained point distribution model, including a mean shape and a covariance matrix, wherein the mean shape represents locations of the set of landmarks in a variety of patients; and applying the point distribution model to the set of landmarks with the locations output from the supervised machine learning algorithm by: removing any landmarks whose locations have an uncertainty above a threshold with reference to the mean shape and covariance matrix; followed by: an optimization of the locations of the remaining landmarks by maximizing joint

Abstract: An apparatus for locating a landmark in a set of image data comprises a landmark location unit that is configured, for each of a plurality of image data items, to obtain from a first two-class classifier a first classification of the image data item as foreground or background, to obtain from a second two-class classifier a second classification of the image data item as foreground or background, and to combine the first classification and the second classification to obtain a combined classification, and wherein the landmark location unit is further configured to use the combined classifications for the plurality of image data items to determine a location for the landmark.

Abstract: An apparatus for locating a landmark in a set of image data comprises a landmark location unit that is configured, for each of a plurality of image data items, to obtain from a first two-class classifier a first classification of the image data item as foreground or background, to obtain from a second two-class classifier a second classification of the image data item as foreground or background, and to combine the first classification and the second classification to obtain a combined classification, and wherein the landmark location unit is further configured to use the combined classifications for the plurality of image data items to determine a location for the landmark.

Abstract: Certain embodiments provide a computer apparatus operable to carry out a data processing method to position a set of anatomical landmarks in a three-dimensional image data set of a part or all of a patient, comprising: providing a trained supervised machine learning algorithm which has been trained to place each of the set of anatomical landmarks; applying the supervised machine learning algorithm to place the set of anatomical landmarks relative to the data set; providing a trained point distribution model, including a mean shape and a covariance matrix, wherein the mean shape represents locations of the set of landmarks in a variety of patients; and applying the point distribution model to the set of landmarks with the locations output from the supervised machine learning algorithm by: removing any landmarks whose locations have an uncertainty above a threshold with reference to the mean shape and covariance matrix; followed by: an optimisation of the locations of the remaining landmarks by maximising joint