Architectural distortion in mammograms is the most frequently missed finding among breast cancer findings, the improvement of detection accuracy in existing commercial CAD software remains a challenge. In this study, in order to improve the detection accuracy of architectural distortion in mammography, we propose a hybrid automatic detection method that combines with the enhancement method of the concentration of line structure and massive pattern. In the method, the detection of the concentration of the line structure is conducted by the adaptive Gabor filter, and the enhancement of the massive pattern is performed by the iris filter. The concentration index is calculated from these filtered images; the lesion candidate regions are obtained. As for false positive (FP) reduction, 15 shape features are calculated from the candidate regions. Then, they are given to the support vector machine; the candidate regions are classified either as true positive or FP. In the experiment, we compared the results of the proposed method and physician interpretation report using 200 images (63 architectural distortions) from a digital database of screening mammography. Experimental results indicate that our method may be effective to improve the performance of computer aided detection in mammography.