Computer-aided detection (CAD) has the potential to aid radiologists in detection of microcalcification clusters (MCs). CAD for digital breast tomosynthesis (DBT) can be developed by using the reconstructed volume, the projection views or other derivatives as input. We have developed a novel method of generating a single planar projection (PPJ) image from a regularized DBT volume to emphasize the high contrast objects such as microcalcifications while removing the anatomical background and noise. In this work, we adapted a CAD system developed for digital mammography (CADDM) to the PPJ image and compared its performance with our CAD system developed for DBT volumes (CADDBT) in the same set of cases. For microcalcification detection in the PPJ image using the CADDM system, the background removal preprocessing step designed for DM was not needed. The other methods and processing steps in the CADDM system were kept without modification while the parameters were optimized with a training set. The linear discriminant analysis classifier using cluster based features was retrained to generate a discriminant score to be used as decision variable. For view-based FROC analysis, at 80% sensitivity, an FP rate of 1.95/volume and 1.54/image were achieved, respectively, for CADDBT and CADDM in an independent test set. At a threshold of 1.2 FPs per image or per DBT volume, the nonparametric analysis of the area under the FROC curve shows that the optimized CADDM for PPJ is significantly better than CADDBT. However, the performance of CADDM drops at higher sensitivity or FP rate, resulting in similar overall performance between the two CAD systems. The higher sensitivity of the CADDM in the low FP rate region and vice versa for the CADDBT indicate that a joint CAD system combining detection in the DBT volume and the PPJ image has the potential to increase the sensitivity and reduce the FP rate.