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In certain situations, it is desirable to separate certain features from the rest of an image.  Normally, these features are called regions of interest (ROI).  Here, the closing and opening morphological operations that were previously used (see here) were utilized to differentiate cancer cells from normal cells.

Note that the closing morphological operation is actually an erosion operation followed by a dilation while opening is a dilation followed by an erosion [1].  If you can remember, the erosion and dilation operations require a structuring element.  In this case, I simply weaved a snippet of code into the while chunk that was the SCILAB program needed for the activity so I could generate a circle which I then took as the structuring element.

Since cancer cells are larger than normal cells (probably because they destroy – a.k.a. eat healthy cells), the normal cell size had to be determined so that you could actually tell which is which.  Call it a calibration of sorts but an image (Fig. 1) was divided into subimages, each with a size of 256 x 256 pixels (Fig. 2).  These subimages were then converted to binary images based on their histogram values.  Since there weren’t pictures of cells lying around, images of paper puncher cutouts were used.

Figure 1. Original image of circles used to determine normal cell size.

Figure 2.  Subimages of size 256 x 256 pixels, shown in their respective locations to the original image

Figure 3.  Results of the binarized subimages obtained through each subimage’s histogram value.

The binarized subimages then went through the two morphological operations, first closing and then opening (Figures 4 and 5).  If I say so  myself, the opening operation cleans up the subimages while the closing operator combines overlapping circles.

Figure 4.  Results of the binarized subimages undergoing the opening morphological operation.

Figure 5. Results of each subimage in Figure 3 undergoing the closing morphological operation.

To determine the actual normal cell size, the area (in pixels²) of each circle – at least each distinguishable area in the image – is obtained (Figure 6).  We can take the peak in the left region to be the percentage of the individual cells and not the clumps which are taken into account by the other histogram peaks, especially those beyond the 2000 pixel count.  The normal size cell is then taken from the assumed individual cells, returning a value of 525. 

Figure 6. The histogram of the number of pixels in each distinguishable area of the image for (top) all pixel numbers and (bottom) zoomed in at 470 to 570 pixels.

The next, and most crucial, part of this whole activity is to detect cancer cells when it’s surrounded by normal cells (Figure 7). 

Figure 7.  Images of normal cells with cancer cells located within them.

Much like the previous step, the image of the cancer and normal cells was binarized based on the histogram values and applied with the opening then closing morphological operation.  Unlike the previous procedure though, the structuring element that was used was a circle that contained more than 525 pixels but lesser than what the approximated size of the cancer cells would be.  I have to say so myself, I isolated those cancer cells pretty well.  But then, I only had my visual count to compare it with.

Figure 8.  Image of the isolated cancer cells from Figure 7.

This activity reminded me so much of the many CSI and Bones episodes I’ve glued my eyes to over the years.  Now, I kind of want to apply this technique on actual medical images.  You can’t always imagine that, in medical images, the cancer cells will be photographed separately from normal cells.  That’s something to look forward to.

9/10 for being able to complete the objectives with the 1-point deduction for being late.

REFERENCES:

1. A10 – Binary Operations, Applied Physics 186 Manual