# Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap smear Images
## 2 Materials and Methods
### Nuclei and Cytoplasm Segmentation  
In this stage, nuclei and cytoplasm regions from each segmented cell that resulted from overlapping cells' segmentation stage were segmented. There were three types of segmented cells that were outputs of overlapping cells' segmentation stage. They were isolated cells, touching cells, and overlapping cells. We divided the nuclei and cytoplasm segmentation into three sub-processes. The first one was the segmentation of the components of the cell from isolated cells. The second one was segmentation from touching cells, and the last one was segmentation from overlapping cells. The cytoplasm boundary of isolated cells, touching cells, and overlapping cells could be extracted from segmented cells' results of the watershed transform approach that was proposed in the overlapping cells' segmentation stage. The regions of touching and overlapping cytoplasm in the image obtained in the segmentation step were not enough to represent the boundaries of the cytoplasm. Thus, we did the process of smoothing the boundaries of the cytoplasm using an edge smoothing method that is described in Table 4.  
The nuclei of isolated cells, touching cells, and overlapping cells were segmented using the shape-based iteration method that is described in Table 5. \begin{table}
\begin{tabular}{l} \hline Step 1: Read grayscale image and convert binary image \\ Step 2: Extract the largest blob only \\ Step 3: Crop-off the frame on the left and top \\ Step 4: Fill holes \\ Step 5: Blur the image \\ Step 6: Threshold again \\ Step 7: Show the smoothed binary image \\ \hline \end{tabular}
\end{table} 
Table 4: Processing steps of edges smoothing method. Figure 9: Results of the proposed cell segmentation algorithm. (**a**) Original color image, (**b**) Image of foreground objects, (**c**) Image of background objects, (**d**) Image of distance transformed, (**e**) Image of over-segmentation result without using markers, (**f**) Markers’ image, (**g**) Image of segmentation result by using markers’ image, (**h**) Image of boundary detection, (**i,j**) Image of cropping results, (**k**) Image of isolated or touching cells, and (**l**) Image of overlapping cells. The nuclei segmentation was easier than cytoplasm segmentation. In our multi-cells image, nuclei were low intensity, and the shapes were well structured, mostly oval or round shape, and significantly different from the other regions, background, or cytoplasm. The area, average intensity, major and minor axis lengths, and solidity were the five most important features of nuclei to distinguish from other objects, cytoplasm, and background. Therefore, we used these features and developed a method to detect and segment nuclei. A median filter was used to remove the noise from the original Pap smear images. The shape-based features (area, intensity values, major axis length, minor axis length, and solidity) were used in the nuclei segmentation process. According to the experiments, the minimum area of nuclei was 362 \(\upmu\)m\({}^{2}\). The average intensity value was between 60 and 150, and solidity values were less than or equal 0.98. The major axis length of nuclei was between 24 m and 117 m, and the minor axis length was between 17m and 87m. Shape-based features' name and their formula are explained in Figure 10b. The results of the nuclei segmentation step are shown in Figures 11 and 12. The performance of our proposed nuclei detection method was evaluated, and their results are shown in Table 6. The nuclei and cytoplasm segmentation results of isolated cells, touching cells, and overlapping cells are described in Tables 7-9, respectively. The proposed overlapping cells' segmentation method had been tested on images with a 4049 cytoplasm in total. About 6.47% of the cytoplasm was isolating, while 35.27% of cytoplasm were touching, and 58.26 % of cytoplasm were overlapping. The rate of well-segmented cytoplasm was calculated by dividing the detected cytoplasm number with the actual cytoplasm number and multiplied by 100, and the results are shown in Table 10.