# An Automatic Analysis System for High-Throughput _Clostridium Diffic_ Toxin Activity Screening
## 3 Results
### Classification Results  
To compare our algorithm with popular methods for cell segmentation, we evaluated the segmentation performance by quantifying errors in the number of cells counted. Accurate segmentation of rounded cells can be measured with morphological cell features such as cell area. However, the adherent cells had irregular borders, which complicated analysis. For the control of performance, we used different types of GFP and DAPI images with various levels of noise and contrast. While building our solution, we compared the algorithm with other segmentation methods, such as active contour models for DAPI images or the region-growing algorithm for GFP images. Segmentation errors of the algorithm led to mis-segmentation rates of 3% of the cells, with errors mostly caused by rounded cells undergoing nuclear fragmentation that were therefore counted as two rounded cells. Diagnostic exactness is often used to mean the fraction of cases on which the automated image analysis system gave a correct answer, where correctness is determined by comparing the diagnostic decision to some definition of \"truth\" [45]. There are different ways to define \"truth\". In our case, we depended on the separate gold standard, which is determination by the results of a pathology expert. We then assessed the accuracy of the designed classification system by comparing its results against the gold standard. The analyzed task is a binary problem, where the answer is \"1\" when the cell is adherent or \"0\" when it is rounded. Table 2 shows the classification results obtained with the implemented algorithm in a confusion matrix. To measure the diagnostic performance, we calculated accuracy, sensitivity, and specificity. The fundamental definitions of these performance measures can be illustrated as follows. The classification accuracy of a technique depends upon the number of correctly classified samples (i.e., true negative and true positive), and is calculated as follows:  
\[Accuracy=\frac{\#TP+\#TN}{N}, \tag{4}\]  
where \(TP\) is the true positive answer, \(TN\) is the true negative answer, and \(N\) is the total number of samples present in the images. The sensitivity (also called the true positive rate--TPR) is the probability that the cell is said to be adherent given that it is. This can be estimated by relative frequency [45; 46]: \begin{table}
\begin{tabular}{l l l l} \hline  & **Adherent** & **Rounded** & **Total** \\  & **(Predicted)** & **(Predicted)** & **Total** \\ \hline Adherent & 2552 & 213 & \\ (Actual) & (\(TP\)) & (\(FP\)) & 2765 \\ \hline Rounded & 164 & 2167 & \\ (Actual) & (\(FN\)) & (\(TN\)) & 2331 \\ \hline  & Sensitivity: 93\% & Specificity: 91\% & Total: 5096 \\ \hline \end{tabular}
\end{table} 
Table 2: List of parameters evaluated by the proposed method. FN: false negative; FP: false positive; TN: true negative; TP: true positive. \[Sensitivity=\frac{\#TP}{\#TP+\#FN}, \tag{5}\]  
where \(TP\) is the true positive answer and \(FN\) is the false negative answer. The value of sensitivity ranges between 0 and 1, where 0 and 1 mean worst and best classification, respectively. On the other hand, we calculated specificity (also called false positive rate--FPR), which is defined as the proportion of the true negatives against all negative results. Specificity is defined by the following equation:  
\[Specificity=\frac{\#TN}{\#TN+\#FP} \tag{6}\]  
where \(TN\) is the true negative answer and \(FP\) is the false positive answer. The value of specificity ranges between 0 and 1, where 0 and 1, respectively, meaning worst and best classification, respectively. Another dominant technique for assessing computer classification performance is the receiver operating characteristic (ROC) analysis [32; 46; 47; 48]. A ROC curve is a plot of sensitivity versus (1--specificity) (i.e., TPR versus FPR; see Figure 10). Area under the ROC curve is the most commonly used accuracy index. The area under a perfect-accuracy ROC curve, when sensitivity and specificity reach 1.0, is also 1.0.