Abstract: A method for identifying a pathological region of a scan (such as a stroke region within a MRI DWI volume scan) is proposed. A region of the scan which is likely to contain pathological tissue (e.g. infracted tissue) is identified by obtaining a parameter which, for a given slice, or portion of a slice, characterizes the distribution of the intensity of pixels, e.g. the relative proportion of high intensity pixels. In a first case, such a parameter is used to identify those slices of a volume scan which are likely to include infarction. In a second case, such a parameter (hemisphere parameter) is obtained for each of the left- and right-hemispheres of a brain, to estimate which hemisphere contains the stroke. In either case, the parameter may be calculated based on ranges, percentiles and functions of the percentiles of the intensity distribution. These ranges, percentiles and functions of the percentiles are not pre-defined but are selected to maximize sensitivity.

Abstract: A method of estimating the location of the anterior and posterior commissures in a brain scan image is proposed. Firstly, a geometrical object is constructed using points on a brain scan image of an individual which are on the surface of the brain, such as an ellipse fitting the cerebral surface of a sagittal image of the mid-sagittal plane, or an adjacent sagittal plane. The locations on the MSP of the AC and PC landmarks (and optionally other landmarks) are estimated using the five parameters which define the ellipse, plus numerical values obtained in advance from statistical analysis of other individuals.

Abstract: A method of segmenting CT scan data comprises transforming intensity data into transformed data values. In a first option, the method includes convolving the CT scan data with a mask to obtain energy data wherein the mask has band pass filter characteristics, generating a histogram of the energy data and segmenting the CT scan data based on energy values in the generated histogram. In a second option, the method includes transforming the intensity data into Hounsfield scale data, and segmenting the image based on predefined Hounsfield scale values.

Abstract: An algorithm is proposed to eliminate from MRI images pixels which have been incorrectly identified as corresponding to infarct material. A first technique is to eliminate identified regions which are determined to be similar to the region of the scan which corresponds to the identified region reflected in the mid-sagittal plane (MSP) of the brain. A second technique is to eliminate regions which are determined not to have corresponding identified regions in one or more of the other scans. The combination of two techniques enhances the confidence in the decision of whether a hyperintense region is an infarct or artifact.

Abstract: A method for identifying a pathological region of a scan (such as a stroke region within a MRI DWI volume scan) is proposed. A region of the scan which is likely to contain pathological tissue (e.g. infracted tissue) is identified by obtaining a parameter which, for a given slice, or portion of a slice, characterises the distribution of the intensity of pixels, e.g. the relative proportion of high intensity pixels. In a first case, such a parameter is used to identify those slices of a volume scan which are likely to include infarction. In a second case, such a parameter (hemisphere parameter) is obtained for each of the left- and right-hemispheres of a brain, to estimate which hemisphere contains the stroke. In either case, the parameter may be calculated based on ranges, percentiles and functions of the percentiles of the intensity distribution. These ranges, percentiles and functions of the percentiles are not pre-defined but are selected to maximise sensitivity.