Improved performance has been reported for computer aided detection (CADe) methods using information from multiple mammographic views over single-view CADe approaches. Linkage across the views is based on assuming that location and image features from the same lesion depicted in both views will be similar. In this study we investigate if the location features can be improved and what effect such an improvement has on the linkage of lesions across ipsilateral views. Performance of different methods to define the location features was first assessed with respect to the location of 137 manually annotated and linked masses. Taking the median result from five complementary methods (based on pectoral muscle boundary, breast shape and intensity signature) increased the mean accuracy compared to the current standard (7.1 vs. 6.3 mm). Thereafter the impact of this best method on the automatic linkage of detected regions across views was assessed for a second, independent dataset of 131 mammogram pairs. Linkage was based on the combination of location and single-view image features by a linear discriminate analysis classifier trained to differentiate between links of corresponding true-positive (TP) regions versus links including TP and false-positive (FP) regions. Nested cross-validation results showed that using the improved location features significantly increased the classification performance and the percentage of correctly linked regions.