In this activity, we aim to segment a particular region of interest from the background. We initially converted the image into binarized form so that the separation of the ROI from the background will be easier, taking into account the optimal threshold. We used the histogram of the image in order to determine the threshold value that will separate the ROI from the background. Figure 1 shows an image depicting normal cells imaged under a microscope. The threshold value used in binarizing the unit normal cell is 0.80. For the 12 subimages, a threshold value of 0.845 was used.
Figure 1. Image of scattered punched paper digitized using a flatbed scanner.
Figure 2. Image of a unit normal cell binarized in order to estimate the area of a unit cell.
This unit normal cell has an area of 530 pixels.
Figure 3. Histogram of the unit normal cell in Figure 2.
Figure 4. Histogram of the Total Normal Cells Area
Figure 5. Segmented Normal Cells
From Figure 5, the mean is found to be 521.85294 pixels with a standard deviation of 18.871387 .
Figure 6 shows an image of normal cells with 5 cancer cells. This image was binarized using a threshold value of 0.845. Figure 7 shows an image of a unit cancer cell. The same threshold value was used to binarized this image of unit cell. Using the histogram shown in Figure 8, the 5 cancer cells were segmented as shown in Figure 9. However, since some of the normal cells are overlapping, their combined area falls on the range of possible area of a cancer cell.
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Figure 6. Normal Cells with 5 Cancer Cells
Figure 7. Unit Cancer Cell. This has an area of 1001 pixels.
Figure 8. Histogram of Total Area with Cancer Cell
Figure 9. Segmented Cancer Cell with some Normal cells.
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