Robustness of computerized identification of masses in digitized mammograms. A preliminary assessment

The authors assess the robustness of a computer-aided diagnosis (CAD) scheme with five rule-based stages to identify regions suspicious for mass in digitized mammograms.

With a database of 428 mammograms, 234 of which had not been analyzed by this scheme before, the authors evaluated the performance robustness of their CAD scheme. The following four issues were investigated to assess the variability of the scheme's performance due to: (1) the maximum permissible number of "masses" detected at each stage; (2) exclusion of selected individual rule-based stages; (3) added image noise; and (4) repeated digitizations of the same image.

Enabling the CAD scheme to select a maximum of two suspicious mass regions at any one stage increased sensitivity by as much as 4% (from 93% to 97%), but it increased the false-positive detection rate by as much as 1.2 per image (from 1.7 to 2.9). Eliminating any individual stage decreased sensitivity by as much as 6%, but this reduced the false-positive detection rate by as much as 0.4 per image (from 1.7 to 1.3). The addition of reasonable noise levels decreased sensitivity by as much as 4% without substantially affecting the false-positive detections. Repeated digitizations of selected images demonstrated a scheme sensitivity of 93% +/- 1.8% with more than a 90% overlap of the false-positive regions.

The results of this preliminary study clearly indicate that this scheme is reasonably robust to the variables investigated here.