Abstract: A method of screening a population of women for breast cancer by mammography, comprising establishing a screening schedule, conducting a screening mammography on a patient in accordance with said screening schedule to obtain a mammogram image, determining whether a cancer lesion is visually observable in said mammogram image, in the event that no cancer lesion is visually observable in said mammogram image conducting a computer image analysis of the mammogram image in which no potential cancer lesion is visually observable, said method serving to determine the risk of there being such a lesion physically present or about to arise within a screening interval, said method comprising applying to the image a statistical classifier trained to score on the basis of texture features in the image that reflect such risk.

Abstract: Volume changes in a brain, brain ventricle, or hippocampus are quantitatively extracted from a comparison of 3D before and after images using an algorithm that distorts a triangulated surface of the before image to produce the surface of the after image and calculates the volume change from the area change.

Abstract: A statistical framework for identifying regions of discrimination between two groups of imaged objects involves determining a weight map defining the regions of discrimination by minimizing the sample size required to discriminate the two groups in a numerical optimization scheme.

Abstract: A method for computer based analysis of a low field MR image including a trabecular region of bone for extracting from said image diagnostic information by applying a trained statistical classifier which has been trained on similar labelled according to the severity of a trabecular bone altering disease suffered at the time or later. For each image in the training set a region of interest (ROI) is defined, textural information relating to the intensities of voxels within the ROI is obtained, and combinations of features of said textural information are found which suitably classify the images according to said labelling. An image under study is treated similarly and features of said textural information for the voxels within the ROI of the image are combined as learnt in the training of the classifier to estimate a level of said trabecular bone altering disease or propensity to develop said bone altering disease or a level thereof associated with said image.

Abstract: A method of manipulation of a representation of a 2-D shape for improving a General Procrustes Alignment process, comprising taking a starting 2-D shape defined by a set of landmarks derived from data representing a 2-D projection image of a body part such as a vertebra, in a suitably programmed computing device deriving for each landmark of the 2-D shape a probable relative depth by the application thereto of a statistical model based on a multiplicity of 3-D shapes defined by landmarks derived from 3-D images of similar said body parts, said landmarks having one depth and two spatial coordinates, said model relating the probable relative depth of each landmark in a 3-D-shape of a said body part to the spatial coordinates of the set of landmarks constituting a said shape, and based on the inferred relative depth of the landmarks of the starting 2-D shape deforming the starting 2-D shape to correct for apparent distortion caused by rotation about an axis parallel to the projection plane of the imaged body par

Abstract: A statistical framework for identifying regions of discrimination between two groups of imaged objects involves determining a weight map defining the regions of discrimination by minimizing the sample size required to discriminate the two groups in a numerical optimization scheme.