Improving CAD performance by fusion of the bilateral mammographic tissue asymmetry information

Bilateral mammographic tissue density asymmetry could be an important factor in assessing risk of developing breast cancer and improving the detection of the suspicious lesions. This study aims to assess whether fusion of the bilateral mammographic density asymmetrical information into a computer-aided detection (CAD) scheme could improve CAD performance in detecting mass-like breast cancers. A testing dataset involving 1352 full-field digital mammograms (FFDM) acquired from 338 cases was used. In this dataset, half (169) cases are positive containing malignant masses and half are negative. Two computerized schemes were first independently applied to process FFDM images of each case. The first single-image based CAD scheme detected suspicious mass regions on each image. The second scheme detected and computed the bilateral mammographic tissue density asymmetry for each case. A fusion method was then applied to combine the output scores of the two schemes. The CAD performance levels using the original CAD-generated detection scores and the new fusion scores were evaluated and compared using a free-response receiver operating characteristic (FROC) type data analysis method. By fusion with the bilateral mammographic density asymmetrical scores, the case-based CAD sensitivity was increased from 79.2% to 84.6% at a false-positive rate of 0.3 per image. CAD also cued more difficult masses with lower CAD-generated detection scores while discarded some easy cases. The study indicated that fusion between the scores generated by a single-image based CAD scheme and the computed bilateral mammographic density asymmetry scores enabled to increase mass detection sensitivity in particular to detect more subtle masses.