We have developed and preliminarily tested a new breast cancer risk prediction model based on computerized bilateral mammographic tissue asymmetry. In this study, we investigated and compared the performance difference of our risk prediction model when the bilateral mammographic tissue asymmetrical features were extracted in two different methods namely (1) the entire breast area and (2) the mirror-matched local strips between the left and right breast. A testing dataset including bilateral craniocaudal (CC) view images of 100 negative and 100 positive cases for developing breast abnormalities or cancer was selected from a large and diverse full-field digital mammography (FFDM) image database. To detect bilateral mammographic tissue asymmetry, a set of 20 initial "global" features were extracted from the entire breast areas of two bilateral mammograms in CC view and their differences were computed. Meanwhile, a pool of 16 local histogram-based statistic features was computed from eight mirror-matched strips between the left and right breast. Using a genetic algorithm (GA) to select optimal features, two artificial neural networks (ANN) were built to predict the risk of a test case developing cancer. Using the leave-one-case-out training and testing method, two GAoptimized ANNs yielded the areas under receiver operating characteristic (ROC) curves of 0.754±0.024 (using feature differences extracted from the entire breast area) and 0.726±0.026 (using the feature differences extracted from 8 pairs of local strips), respectively. The risk prediction model using either ANN is able to detect 58.3% (35/60) of cancer cases 6 to 18 months earlier at 80% specificity level. This study compared two methods to compute bilateral mammographic tissue asymmetry and demonstrated that bilateral mammographic tissue asymmetry was a useful breast cancer risk indicator with high discriminatory power.