3. The method according to claim 2, wherein said cluster filtering step comprises: a step of area-point transformation in which signals representing possible microcalcifications are reduced to respective single pixels in a first binary image; a step of counting the number of pixels exceeding a predetermined value in a kernel of a predetermined size scanned across said first binary image and copying the contents of the said first binary image that lay within the kernel to the corresponding locations in a second binary image whenever the contents of the kernel meet a predetermined grouping criteria based on said counting; outputting a second binary image comprised of the pixels remaining after the counting and eliminating step.
3. The method according to claim 2, wherein said cluster filtering step comprises: a step of area-point transformation in which signals representing possible microcalcifications are reduced to respective single pixels in a first binary image; a step of counting the number of pixels exceeding a predetermined value in a kernel of a predetermined size scanned across said first binary image and copying the contents of the said first binary image that lay within the kernel to the corresponding locations in a second binary image whenever the contents of the kernel meet a predetermined grouping criteria based on said counting; outputting a second binary image comprised of the pixels remaining after the counting and eliminating step. 4. The method according to claim 3, wherein said cluster filtering step comprising: selecting the size of said kernel so as to discriminate against false clusters represented by pixels in the first binary image separated by greater than a predetermined distance.