Abstract: A method for the automated analysis of digital images, particularly for the purpose of assessing mitotic activity from images of histological slides for prognostication of breast cancer. The method includes the steps of identifying the locations of objects within the image which have intensity and size characteristics consistent with mitotic epithelial cell nuclei, taking the darkest 10% of those objects, deriving contours indicating their boundary shape, and smoothing and measuring the curvature around the boundaries using a Probability Density Association Filter (PDAF). The PDAF output is used to compute a measure of any concavity of the boundary—a good indicator of mitosis. Objects are finally classified as representing mitotic nuclei or not, as a function of boundary concavity and mean intensity, by use of a Fisher classifier trained on known examples.

Abstract: A method of target recognition performs a 3D comparison of target and reference data. Translation invariant signatures are derived from the two data sets, and an estimate of the orientation of the target with respect to the reference is obtained. Rotational alignment and comparison can then be achieved. The 3D data sets can be represented on an axi-symmetric surface such as a sphere and rotational convolution, over a discrete set of selected rotation angles can be performed. Optic flow can be used to derive the estimate of orientation or the target relative to the reference, in terms of a displacement field.

Abstract: A method for the automated analysis of digital images, particularly for the purpose of assessing nuclear pleomorphism from images of histological slides for prognostication of breast cancer. The method includes the steps of identifying the locations of objects within the image which have intensity and size characteristics consistent with epithelial cell nuclei and deriving boundaries for those objects. Statistics concerning at least the shapes of the derived boundaries are calculated and clutter is rejected on the basis of those statistics and/or they are used to assign probabilities that the respective objects are epithelial cell nuclei, and a measure of the variability of at least the areas enclosed by such boundaries is then calculated.

Abstract: A method of target recognition performs a 3D comparison of target and reference data. Translation invariant signatures are derived from the two data sets, and an estimate of the orientation of the target with respect to the reference is obtained. Rotational alignment and comparison can then be achieved. The 3D data sets can be represented on an axi-symmetric surface such as a sphere and rotational convolution, over a discrete set of selected rotation angles can be performed. Optic flow can be used to derive the estimate of orientation or the target relative to the reference, in terms of a displacement field.

Abstract: A method for the automated analysis of digital images, particularly for the purpose of assessing nuclear pleomorphism from images of histological slides for prognostication of breast cancer. The method includes the steps of identifying the locations of objects within the image which have intensity and size characteristics consistent with epithelial cell nuclei and deriving boundaries for those objects. Statistics concerning at least the shapes of the derived boundaries are calculated and clutter is rejected on the basis of those statistics and/or they are used to assign probabilities that the respective objects are epithelial cell nuclei, and a measure of the variability of at least the areas enclosed by such boundaries is then calculated. In one embodiment the boundaries are derived by seeking closed contours consisting of points of the same grey-level within regions centred on the locations of such objects.