# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories
## 4 Methods
### Segmentation of Breast Tumor  
In the segmentation stage, we only focus on how to detect effectively true tumor regions for latter processing such as feature extraction, and classification. To this end, the tumor Region Of Interest (ROI) needs to be located beforehand. An initial segmentation based on K-means clustering [18] was performed to detect the suspicious tumor areas including tumor and background which is composed of fat, skin, muscle, glands and other tissues, as shown in Figure 5e. Since the dark tissues around a tumor are easy to be mis-classified as candidate tumors after K-means clustering, eliminating the pixels which do not belong to the tumor tissue using some processing operations is necessary in order to improve the clustering accuracies. In this step, operation based on morphological dilation reconstruction was applied to remove the regions touching the border of the image, and objects with smaller area were considered as artifacts and further filtered out. The remaining regions may thus be considered as suspicious/candidate tumor or lesion regions, as shown in Figure 5f.  
Unfortunately, after the previous step, some suspicious tumor regions may be preserved yet. Clinically, a tumor may be found as an object having a round shape. In order to detect an actual tumor region, a region ranking method [3] based on scoring in terms of area similarity and circularity was applied on Figure 5f to determine the region whose ranking score was greater than a specified threshold. Thus, candidate tumor areas may be further selected to be tumor areas, as shown in Figure 5g. However, there exist holes or irregular shapes in the detected tumor regions due to non-uniform intensities caused by different skills or experience in fetching images. Seed regions inside these candidate regions may be determined and used as reference points for region growing [19]. In order to resolve this problem, we used the growing method proposed by [PERSON] [3]. The seed point is used as a start point,  
Figure 5: Contour detections for the sample tested for BI-RADS 2 category with Model B: (**a**) original; (**b**) SRAD; (**c**) image normalization; (**d**) image enhancement; (**e**) k-means clustering; (**f**) result after removing regions with small areas or regions touched with image border; (**g**) result image after region ranking; (**h**) result image after region growing.  
and a region will be expanded gradually by observing its neighborhood pixels' intensity values and gradient magnitudes. This operation can be expressed as Equation (1):  
\[G(v)\leq max(\frac{M}{b_{2}},min(b_{1}\times m,M)) \tag{1}\]  
where \(G(v)\) is the intensity value of pixel \(v\), \(m\) is the intensity mean of region, and \(M\) is the intensity mean of the entire image. In our experiment, we used \(b_{1}=1\) and \(b_{2}=1.9\) as our parameters. Nevertheless, the proposed approach still cannot accurately detect the tumor acquired by difference ultrasound machines. Therefore, the growing method is further modified from Equation (1) to Equation (2) in order to resolve the problem coming from different machines:  
\[G(v)\leq\frac{(M+m)}{2}-b \tag{2}\]  
where \(G(v)\) is the intensity value in pixel \(v\), \(m\) is the average intensity of the growing area, \(M\) is the average intensity of the entire image, and \(b\) is criterion parameter for halted growing. According to the different sonogram models, the average parameter of b is determined by various experiments which are set as 7, 18, and 14 for Model B, Model B, and Model C, respectively. After performing the region growing, an actual breast tumor can thus be detected, as shown in Figure 5h. Resultant images of the detected tumor contour with different machines are shown in Figure 6, respectively. Therefore, the proposed breast ultrasound CAD system not only can segment multi-tumors, but also can be suitable for different types of breast ultrasonic scanners. Especially, it even can detect multiple tumors automatically.