# Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap smear Images
## 2 Materials and Methods
### Cells Segmentation  
The aim of this step was to segment the regions of the cell from input images. The nuclei and cytoplasm are important components in the cell region. In a Pap smear screening system, cytologists examine the microscope images of cells and label the cells into cancer or normal cells based on the appearance of cells components. The automated screening system is also the same procedure. The segmentation of cell components is an important step in the automated detection system. There are many difficulties in the multi-cells segmentation process, such as overlapping cells or including unwanted artifacts. The nuclei segmentation is 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. But the major issues in cytoplasm segmentation are overlapping boundary and poor contrast. Most studies have focused on nuclei segmentation, and rarely studies have focused on cytoplasm segmentation. In Pap smear analysis, the characteristics of cytoplasm are very important. We used the marker-controlled watershed algorithm for cytoplasm segmentation to solve the issue of overlapping boundary detection and splitting of touching cells into individual cells. The main problem of the standard watershed transform is over-segmentation. To avoid this problem, we used markers. The marker image was a binary image with one marker point or multiple points. The flowchart of the proposed modified watershed transform algorithm is described in Figure 6.  
Figure 4: Multi-cells images of five classes: (**a**) Superficial-Intermediate cells, (**b**) Parabasal cells, (**c**) Metaplastic cells, (**d**) Dyskeratotic cells, and (**e**) Koilocytotic cells. Figure 5: Image enhancement, (**a**) original image, (**b**) noise removal by the medial filter, and (**c**) contrast enhancement by CLAHE (contrast limited adaptive histogram equalization). In our proposed overlapping cells' segmentation method, there were ten stages to segment the multi-cells images into individual cells that were used for the nuclei and cytoplasm regions extraction. The summary of each step is shown in Table 3. As first, the original images were changed into gray images. The next step was foreground and background markers extraction and then segmentation using watershed transform function and viewing the segmented results. For foreground and background markers calculation, morphological operations based techniques were used. To separate overlapping cells into individual cells, the boundary of cytoplasm regions was detected after overlapping the cells' detection stage. Then, the area of each cell was detected by thresholding the predefined minimum and maximum area values to remove the unwanted object areas. After that, the detected regions were cropped by a bounding box, and the cropped regions were classified into three classes using unsupervised machine learning, k-means with six intensity-based features. The intensity variation of three groups of cell patches (isolated, touching, and overlapping cells) were significantly different, as shown in Figure 7. So, we used six intensity-based features (mean, variance, skewness, kurtosis, energy, and entropy). \begin{table}
\begin{tabular}{l} \hline \hline
**Step 1:** Read color image and convert gray image \\
**Step 2:** Mark the foreground objects \\
**Step 3:** Compute background objects \\
**Step 4:** Use markers’ image that is roughly in the middle of the cells to be segmented \\
**Step 5:** Compute the watershed transform of makers’ image \\
**Step 6:** Show the result of detected overlapping cells’ regions \\
**Step 7:** Calculate the boundaries of detected regions in the image \\
**Step 8:** Detect areas between the minimum and maximum values for cells regions \\
**Step 9:** Cropping the regions \\
**Step 10:** Classify the regions of the cell into isolated, touching, or overlapped cells \\ \hline \hline \end{tabular}
\end{table}