# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories
## 4 Methods
### Feature Extraction  
Once the actual tumor contour has been located, how to extract robust features is important for correct BI-RADS classification. Here, morphology features and texture features which correspond to the BI-RADS descriptors are defined and measured. The flow chart of the feature extraction stage is illustrated in Figure 7. A total of 145 features composed of their corresponding descriptors, including shape features, orientation features, margin features, echo pattern, posterior features, etc., as mentioned in the Table 4, were extracted to identify the BI-RADS category. These 17 features corresponding to morphology, such as shape, orientation, and margin, were  
Besides, texture can be efficiently used to describe echo pattern and the posterior, as mentioned in Table 4. This is due to the fact that it can measure these two feature categories using the spatial intensity distribution of the tissues or using degree of degradation when the ultrasound signals penetrate the tissues. In general, gray level co-occurrence matrix (GLCM) [4] based on orientation decomposition is a good measure for texture feature, and is evaluated often by distances and orientation between neighboring pixels. Here, GLCM at four directions (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\)) and one distance (\(d=1\) pixel) are first constructed; \(\mathbf{G}\) is referred to as the matrix. Then, an estimate of the probability \(p_{ij}\) is computed from the elements of \(\mathbf{G}\). A variety of texture descriptors used for characterizing the contents of GLCM, including contrast, correlation, energy, entropy, homogeneity, sum average, and sum entropy, were computed and averaged, respectively. Therefore, a total of seven features were chosen when we performed GLCM on the original image. Besides, multi-resolution wavelet transform (adopting eight images) and multi-resolution ranklet transform (adopting nine images) were also performed using the same descriptors measured from the same operations by using GLCM, respectively. Finally, a total of 126 features were generated from the original image, the multi-resolution wavelet transform, and the multi-resolution ranklet transform. Because the posterior feature can be used to evaluate the echo variations behind the tumor, it can be often measured directly from the intensity distributions close to the tumor region via computing the difference of the average between the upper and lower area, and the difference of the average between the right and the left area (generating two features). Finally, a total of 128 mass features corresponding to BI-RADS descriptors such as echo pattern and posterior features were extracted in this stage [4]. However, only the SVM classifier needs to be performed using a feature selection stage, such as feature dimension reduction, and feature scaling, in order to exclude unnecessary or insignificant features, because the classifier itself does not have the capability of feature selection.  
Various features related to lesion or disease severities corresponding to the BI-RADS categories were fed into three machine learning and deep learning classifiers, including: Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) with/without feature selection operation to develop a multi-class assessment of breast tumor severity based on BI-RADS, as shown in Figure 8. From these three classifiers, a classifier evaluated with the highest performance was chosen as our final classification decision for the proposed CAD system.  
Figure 7: The flowchart of the BI-RADS feature extraction stage.