## 2 CAD System Overview
### Removal of non-colon segments  
At this stage, the air outside the body is easily discarded from the mask by detecting the largest connected component touching the image border. An example after this operation is shown in Figure 3(b). From the remaining voxels, a bounding box \(B_{\mbox{body}}\) (used later) is determined and encloses the body of the patient. Next, the remaining 3D regions are analyzed in order to separate the colon from the remaining non-colon regions. Care is taken to design the non-colon segment removal for high sensitivity; that is, it is preferable to include a small non-colon region than to remove a colon region. Although this may later lead to false positive CAD marks outside the colon, such CAD marks are easily dismissed by the clinical reader and significantly less important than false negatives in the colon.  
The lung detection and removal algorithm analyzes 2D axial slices, moving from the top of the volume down towards the rectum. Lung detection is primarily based on two features of the lungs:  
1. The lungs contain many small blood vessels, which appear as small \"holes\" in the segmentation mask.
2. The lungs are continuous anatomic regions and therefore should have significant overlap in adjacent axial slices.  
Figure 3: Thresholding (a) and outer air removal (b) demonstrated on an axial slice that includes the lungs.  
On a 2D slice, a small region is considered to be a _hole_ if it is contained within a labeled region and consists of \(T_{h}\) or more connected background pixels; holes appear as small dark regions as shown in Figure 3 (b). A 3D labeled region is considered to be lung if it contains at least \(T_{l}\) holes. \(T_{h}\) and \(T_{l}\) were set to \(2\) and \(10\) experimentally. Once the lung regions are detected on each 2D slice, the amount of overlap on adjacent slices is determined. Starting with the topmost slice in the volume, the degree of overlap of the \(i\)th labeled region \(R_{i}\) is determined with lung regions on the next slice, that is,  
\[\text{Overlap}(R_{i},R_{i+1})=\frac{R_{i}\cap R_{i+1}}{R_{i}}>T_{r} \tag{1}\]  
where \(T_{r}\) is a threshold set as \(0.35\). If the above condition holds, \(R_{i}\) is removed from the segmentation mask.  
The stomach is located near the lung area and near the transverse colon and often appears as a large hollow object in the CT scan. A 3D labelled object is identified as stomach if it only appears in the top (as measured in the \(z\)-direction) one third of the volume. After detection, the stomach is removed from the segmentation mask.  
Small bowel can be detected and removed from the segmentation mask based on the following observations:  
1. Small bowel segments tend to have smaller volume than large bowel segments.
2. In a coronal view, the colon frames (surrounds) the small bowel, as shown in Figure 4.  
Based on these observations, we first remove any segment below \(T_{v}=30,000\) mm\({}^{3}\), which eliminates much of the small bowel. However, when the colon is sufficiently distended, some small bowel segments may still remain in the segmentation mask. A second filter is applied using the  
Figure 4: Illustration of the small bowel (intestine) and colon. Notice that the small bowel is framed by the colon in this coronal view. Public domain image courtesy of the U.S. government.  
bounding box \(B_{\mbox{body}}\), which is reduced \(T_{s}=100\)mm on all six sides, forming a new bounding box \(B_{s}\). Any segment with bounding box \(B_{i}\) that is completely contained in \(B_{s}\) is removed from the segmentation mask.