## 3 Cell Extraction
### Experiments  
A database of 100 images, collected from the Tokyo Center for lung cancer, was utilized in this study. The size of each image is 768 \(\times\) 512 pixels and they were provided in the RGB space. Furthermore, for each image a mask was manually made as a ground truth data, dividing the images into sputum and non-sputum segments. These images were obtained by manually selecting the regions of interest by masking the location of the corresponding pixels in binary images. Thus, a ground truth image is a binary image where one and zero corresponds to an ROI pixel and to a background pixel, respectively. The ground truth images are used in comparison with the output images from the detection algorithms. We conducted a comprehensive set of experiments to study the outcome of the threshold algorithm for the detection and extraction of the cells into sputum cells and background. Furthermore, we analyzed the essence of color representation and color quantization on the sputum cell detection. Thenwe used the cell extraction techniques (threshold classifiers and Bayesian classification). In the case of the Bayesian classifier, 10-fold cross validation was used: the dataset was randomly divided into 10 blocks; for every hold-out block, the system was trained on the remaining blocks and tested on the hold-out block; results averaged over all test blocks, thus reflect predictive performance. For performance measurement we first computed the true positives (_i.e._, pixels that were correctly classified as sputum pixels TP), false positives (_i.e._, pixels that were erroneously classified as sputum pixels FP), true negatives (_i.e._, pixels that were correctly classified as non sputum pixels TN), and false negatives (_i.e._, pixels that were erroneously classified as non sputum pixels FN). Further measurements were based on these criteria [21]. \[\text{Sensitivity} =\frac{TP}{TP+FN}\] \[\text{Specificity} =\frac{TN}{TN+FP}\] \[\text{Accuracy} =\frac{TP+TN}{TP+TN+FP+FN}\]  
The sensitivity reflects the extent to which pixels classified as sputum pixels are actually sputum pixels. Specificity measures how well the background is classified, and the accuracy evaluates the overall correctly classified pixels. Table 1 contains the sensitivity results obtained by using 10-fold cross validation in Bayesian classification with histogram analysis. Every column contains a different color space (RGB, YCbCr, HSV and L*a*b*) and every row represents a different histogram resolution (16, 32, 64, 128 and 256). The comparative analysis focuses on the index \(v\) and the index \(dev\), which corresponds to the average and standard deviation of the performance indicators respectively. Quantitatively, for Bayesian classifier the ROC curves have been computed for four color representations for five histogram resolutions. The ROC curve is the parametric curve which contains False Detection Rate (\(t\)), and Correct Detection Rate (\(t\)) where \(t\) is a classifier parameter. The curves \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RGB** & **YCbCr** & **HSV** & **L*a*b** \\ \hline \multirow{2}{*}{**Histogram Resolution**} & \(V\) & \(V\) & \(V\) & \(V\) \\ \cline{2-5}  & _dev._ & _dev._ & _dev._ & _dev._ \\ \hline \multirow{2}{*}{16} & 0.86 & 0.85 & 0.88 & 0.81 \\  & 0.14 & 0.15 & 0.12 & 0.18 \\ \hline \multirow{2}{*}{32} & 0.88 & 0.82 & 0.88 & 0.87 \\  & 0.12 & 0.17 & 0.12 & 0.13 \\ \hline \multirow{2}{*}{64} & 0.88 & 0.88 & 0.89 & 0.87 \\  & 0.12 & 0.12 & 0.11 & 0.12 \\ \hline \multirow{2}{*}{128} & 0.89 & 0.88 & 0.89 & 0.88 \\  & 0.11 & 0.12 & 0.12 & 0.11 \\ \hline \multirow{2}{*}{256} & 0.89 & 0.89 & 0.89 & 0.88 \\  & 0.11 & 0.11 & 0.11 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The average and standard deviation of the sensitivity for each color space using 10-fold cross-validation. are depicted in Figure 5. As can be observed from Table 1 and Figure 5 that, the HSV has the best performance across all the resolutions, followed by the RGB.