## 4 Experimental Results  
### Data Set Details and Feature Selection  
To assess the classification accuracy of _WP-SYM_, we used volumes of interest (VOIs) representing lesion candidates in clinical CTC data sets. The VOIs were labeled into true polyps (TP) and false positives (FP) by expert radiologists. The CTC data used, was acquired by use of helical single-slice and multi-slice CT scanners (GE HiSpeed CTi, LightSpeed QX/I, and LightSpeed Ultra; GE Medical Systems, Milwaukee, WI). The patients' colons were prepared with standard laxative pre-colonoscopy cleansing and scanned in supine and prone positions with collimations of 1.25 - 5.0 mm, reconstruction intervals of 1.0 - 5.0 mm, X-ray tube currents of 50 - 260 mAs with 120 - 140 kVp, in-plane voxel sizes of 0.51- 0.94 mm, and a CT image matrix size of 512 x 512. Two CTC scan positions (supine and prone) are generally used for each patient to improve the specificity of polyp detection through improved differentiation of mobile residual stool from polypoid lesions [31; 32]. We further divided the VOIs in the TP class into two categories: medium-size polyps that were between 6-9 mm in size (hereafter, TP1), and large polyps \(\geq\)10 mm (hereafter, TP2). This partition was determined by correlating the CTC data with colonoscopy reports. The motivation for this size-based partitioning is that in colorectal screening, large polyps are considered to require polypectomy, whereas for smaller polyps a follow-up surveillance may suffice. A total of 61 colonoscopy-confirmed polyps measured 6 mm or larger: 28 polyps were identified as TP1, and 33 polyps as TP2. The number of entries in the TP class would be higher than the number of actual polyps and this is because the lesion may be seen in both supine and prone positions, and because some large lesions could be represented by more than one detection.  
To compare the classification performance of _WP-SYM_ with previously published CAD results, and to confine the variability to the classifier method itself, we used the technique proposed by our earlier work in [2]. As for the feature extraction technique, we adopted the 3D CAD scheme also developed earlier in [2] that extracts a thick region encompassing the entire colonic wall in an isotropic CTC volume. Discriminative geometric features (shape index, curvedness, CT value, gradient, gradient concentration, and directional gradient concentration, where each of which is characterized by nine statistics) identify polyps at each voxel of the extracted colon and are used for detecting polyp candidates. Figure 3 (a) represents an axial CT slice where the white box indicates a region of interest  
\begin{table}
\begin{tabular}{c c c} \hline \hline Symbol & Name & Count \\ \hline \multirow{4}{*}{DB1} & \multirow{4}{*}{Database 1} & Class 1 (FP) = 8008 \\  & & Class 2 (TP1) = 43 \\  & & Class 3 (TP2) = 84 \\ \cline{1-1}  & & VOI=16*16*16*16*4096 Features \\ \hline \hline FP: False Positive, TP1: True Positive (medium-size polyps: 6 – 9 mm); TP2: True Positive (polups \(\approx\)10 mm); VOI: Volume of Interest \\ \hline \end{tabular}
\end{table}
Table 3: Database properties.  
with a polyp whereas Figure 3 (b) is a magnification of the region of interest with the polyp indicated by a white arrow. Folds are shown in light gray and colonic wall in dark gray. Suspicious regions identified by connected components are further segmented by use of hysteresis thresholding followed by fuzzy clustering to identify true polyps from non-polyps.