Several studies have demonstrated the fractal properties of screening mammograms. The purpose of this study was to investigate fractal texture analysis for the automated detection of architectural distortion (AD) in screening mammograms. The study was based on the Digital Database for Screening Mammography (DDSM). Initially, a database of 708 mammographic regions with confirmed pathology was created. They were all 512x512 pixel regions of interest (ROIs). The ROI size was determined empirically. Fifty-two regions were extracted around biopsy-proven architectural distortion. The remaining 656 ROIs depicted normal breast parenchyma. Fractal analysis was performed on each ROI at multiple resolutions (512x512, 256x256, 128x128, and 64x64). The fractal dimension of each ROI was calculated using the circular average power spectrum technique. Overall, the average fractal dimension (FD) estimate of the normal ROIs was statistically significantly higher than the average FD of the ROIs with AD. This result was consistent across all resolutions. However, best detection performance was achieved when the fractal dimension was estimated on ROIs subsampled with a factor of 2 (ROC area index Az=0.89±0.02). Specifically, there was perfect performance in fatty breasts (Az=1.0), Az=0.95±0.02 in fibroglandular breasts, Az=0.84±0.05 in heterogeneous breasts, and Az=0.66±0.10 in dense breasts. Overall, the present study demonstrates that the presence of AD disrupts the normal parenchymal structure, thus resulting in a lower fractal dimension. Consequently, fractal texture analysis could play an important role in the development of computer-assisted detection tools tailored towards architectural distortion.