Mass detection in mammographic ROIs using Watson filters
Human vision models have been shown to capture the response of the visual system; their incorporation into the classification stage of a Computer Aided Detection system could improve performance. This study seeks to improve the performance of an automated breast mass detection system by using the Watson filter model versus a Laguerre Gauss Channelized Hotelling Observer (LG-CHO). The LG-CHO and the Watson filter model were trained and tested on a 512×512 ROI database acquired from the Digital Database of Screening Mammography consisting of 800 total ROIs; 200 of which were malignant, 200 were benign and 400 were normal. Half of the ROIs were used to train the weights for ten LG-CHO templates that were later used during the testing stage. For the Watson filter model, the training cases were used to optimize the frequency filter parameter empirically to yield the best ROC Az performance. This set of filter parameters was then tested on the remaining cases. The training Az for the LG-CHO and the Watson filter was 0.896 +/- 0.016 and 0.924 +/- 0.014 respectively. The testing Az for the LG-CHO and Watson filter was 0.849 +/- 0.019 and 0.888 +/- 0.017. With a p-value of 0.029, the difference in testing performance was statistically significant, thus implying that the Watson filter model holds promise for better detection of masses.
