## 1 Introduction  
In recent years, the incidence and mortality of breast cancer have increased to a great extent, and it has become a major public health problem [1]. According to a report released by the International Agency for Research on Cancer Research (IARC) in 2012, more than 1.67 million women are affected by breast cancer every year in the world, and this number is growing [2]. If the breast cancer is detected early, the 5-year relative survival rate is 99%. If the cancer spreads to a distant part of the body, the 5-year survival rate is 27% [3]. Hence, early diagnosis of breast cancer is particularly important. At present, the digitized screen-filmed mammography is the most effective tool for early detection of breast cancer. However, due to the complex structure of the breast and the quality of the mammograms, doctors are prone to misdiagnosis at the time of diagnosis. Therefore, the computer-aided diagnosis (CAD) system comes into being. A suitable CAD system can provide effective help for radiologists during diagnosis, reduce the workload of doctors, and improve the accuracy of diagnosis [4]. The characteristics of breast cancer mainly include three parts: Mass, calcification, and architectural distortion (AD). AD is a distortion of the mammary gland when the lesion destroys the normal structure of the mammary gland but there is no mass [5]. Due to the complexity of the cause of AD, the limitations of image quality and doctor experience during the examination, radiologists often misjudge AD [5]. Thus, the detection and analysis of AD has become a research hot-spot in the field of CAD breast cancer. Recently, many computer-aided AD detection algorithms have been proposed. [PERSON] et al. proposed a fractal analysis method to distinguish normal breast and AD of the region of interest (ROI), however, this method required ROI to be set in advance [6]. [PERSON] et al. proposed using Gabor filters and phase portrait to detect AD by analyzing the texture direction of the mammograms, with a sensitivity of 84% [7]. [PERSON] et al. proposed a Geometrically Constrained Phase Portrait Model (GCPPM) method, which greatly reduced the false positive rate [8]. [PERSON] et al. proposed a method for detecting AD using the direction texture obtained by the Gabor filter group, with a sensitivity of 88% [9]. [PERSON] et al. proposed a Gaussian Mixture Model (GMM) to detect AD with a sensitivity of 84% [10]. [PERSON] et al. proposed using contourlet transform and neural network to detect AD, with an accuracy of 64% [11]. [PERSON] et al. proposed employing the angular deviation degree of the linear structure of breast tissue as a feature to detect AD [12]. [PERSON] et al. proposed to calculate the similarity convergence index of breast speculations for detecting AD, with a sensitivity of 88.9% [13]. [PERSON] et al. proposed using a high-pass isotropic filter to detect suspicious regions of AD, and identified AD by detecting contours of suspicious regions [14]. [PERSON] et al. proposed using an adaptive Gabor filter to detect AD, and the filter parameters were adjusted according to the mammary structure [15]. [PERSON] et al. proposed to extract the line structure from the mammograms by the normal curvature for detecting AD [16]. [PERSON] et al. proposed using geometrical properties of edge structures for detecting AD [17]. [PERSON] et al. extracted the linear structure information in the ROI and the edge, and formed a new feature vector according to different weights, AD was detected by SVM, and with an accuracy of 89% [18]. [PERSON] et al. proposed using a radial ridge to detect AD based on the linear characteristics, with a sensitivity of 85% and with a specificity of 80% [19]. [PERSON] et al. proposed using deep learning to detect architectural distortion in mammograms, however, due to the limitation of the dataset size, the accuracy is only 86.1% [20]. These mentioned techniques have made a great contribution.