1. A method for detecting clustered abnormalities in a digitized medical image comprising the steps of: receiving digital image data, said image data representing a medical image and comprising a plurality of pixels, each pixel having a gray-scale value based at least in part on the brightness of the pixel; obtaining an output value for at least some of the pixels, the output value of a pixel being based at least in part on the contrast of the gray-scale value of the pixel relative to the gray-scale values of nearby pixels; identifying a first set of seed pixels by applying a first threshold function to the output values of at least some of the pixels; identifying a first set of groups of pixels by applying a second threshold function to the output values of a plurality of pixels in close proximity to at least one pixel of the first set of seed pixels; identifying a second set of seed pixels by applying a third threshold function to the output values of a plurality of pixels within a predetermined distance to at least some pixels of the first set of groups, said third threshold function being less selective than the first threshold function; identifying a second set of groups of pixels by applying a fourth threshold function to the output values of a plurality of pixels in close proximity to at least one pixel of the second set of seed pixels; and identifying potential abnormalities in the medical image according to at least the distance between at least some of the groups in the first and second sets of groups of pixels.