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 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 for the automated analysis of digital images, particularly for the purpose of assessing the presence and severity of cancer in breast tissue based on the relative proportions of tubule formations and epithelial cells identified in digital images of histological slides. The method includes the step of generating a property co-occurrence matrix (PCM) from some or all of the pixels in the image, using the properties of local mean and local standard deviation of intensity in neighbourhoods of the selected pixels, and segmenting the image by labelling the selected pixels as belonging to specified classes based upon analysis of the PCM.