## 6 Classification
### Data and training  
The CT colonography data in our experiments consisted of \(382\) patients (\(764\) CT volumes) of prone and supine volumes collected from \(8\) institutions. The data is highly diverse. CT images were generated using scanners from all the major manufacturers, including Siemens, GE, Philips, and Toshiba, with \(4\), \(8\), \(16\), \(32\), and \(64\) multi-slice configurations, as described in Table 2. KVp ranged from \(120-140\), and exposure ranged from \(29\)-\(500\) mAs. Patients were placed in feet first prone and supine, head first prone and supine, as well as decubitus positions. All subjects were scanned within the last 10 years (1999-2008) and roughly \(80\%\) were administered fluid and fecal tagging. When measuring performance of the CAD, the sensitivity is measured per polyp (that is, if a polyp is detected on either or both volumes it is considered a true positive) and false positives are measured per volume, as is the convention in CTC. Per patient false positive rates can be determined by multiplying the per volume false positive rate by \(2\).  
This data was split into two subsets: _dataset1_, containing \(180\) patients (\(360\) volumes) for training and _dataset2_, containing \(202\) patients (\(404\) volumes) for independent testing. On each slice, the average  
\begin{table}
\begin{tabular}{c c c} \hline Manufacturer & Model & Multi-slice configurations \\ \hline Siemens & Somatom & \(4\), \(16\), \(64\) \\ GE & Lightspeed & \(4\), \(8\), \(16\) \\ Philips & Brilliance & \(4\), \(64\) \\ Toshiba & Aquilon & \(16\), \(32\), \(64\) \\ \hline \end{tabular}
\end{table}
Table 2: Scanning equipment used to produce the data.  
pixel dimensions were \(0.73\) mm by \(0.73\) mm, and on this varied set of data, the slice thickness ranged between \(0.6\) mm to \(1.25\) mm. _Dataset1_ had \(168\) polyps \(\geq 6\) mm in size, and \(102\) polyps \(\geq 10\) mm in size. _Dataset2_ had \(119\) polyps \(\geq 6\) mm in size, and \(75\) polyps \(\geq 10\) mm in size. We randomly divided _dataset1_ as \(70\%\) for training and \(30\%\) for validation, which was used to test and adjust the training to maximize performance. We also tested the constructed model on _dataset2_. For ground truth, three medical experts performed a consensus annotation of the scans.  
The training data was randomly divided into \(50\%\) for training AdaBoost and \(50\%\) for determining the objective performance for each node. The candidate generation algorithm typically produces a few thousand candidates, which are then classified using the [PERSON]72\) features described in the previous section[PERSON] At the first node of the cascade, a four feature subset was selected by MRMR from the cost-efficient feature set and trained by AdaBoost. These selected features are variance of intensity, shape index cluster size, and mean and variance of shape index[PERSON] The selected features provide an intuitive meaning for false positive filtering, as described in [PERSON] _et al._'s paper Yoshida et al. (2010), which applies shape index clustering for candidate generation. After the first stage, any feature may be used by the cascade when required by the classifier.  
In testing, the feature calculation is interleaved with the cascade classifier. At any particular stage in the cascade, we only compute the required features[PERSON] The full set of features is only computed for the regions that are hardest to classify, resulting in computational savings.