The Standard Deviation Filter Nor Dark Field Thresholds - Engineering Assessment Answer

Engineering Assessment Task

1. Load the Matlab data file CellMovie.mat. Note that Matlab will format the movie as a structure array where the grey scale image for each frame is stored in the ‘cdata’ field.

2. Segment the foreground (cells) from the background by combining a standard deviation filter (see the Matlab function stdfilt) and a dark field filter which identifies cell nuclei and cell boundaries. a. Standard deviation filter Filter the 7 images using a standard deviation filter and select all pixels with a standard deviation greater than 8 as the foreground.

b. Dark field filter Select all pixels whose grey scale intensity is less than the 5th percentile for each image of the movie (hint: use the prctile function). You should generate a montage of all 7 images.

c. Overlay these binary images onto the each grayscale movie image using the function labeloverlay (hint: allocated memory for the rgb movie using the instruction zeros(r,c,3,n,'uint8') where r, c and n are the number of rows, columns and movie frames.

3. As you can see above, neither the standard deviation filter nor dark field thresholds clearly segment cell bodies. In this next step we will apply morphological operations to fill in the gaps to remove image speckle that is not part of cell bodies. a. Close both the standard deviation and dark field binary images using a disk structural element with radius 2 pixels and each image of the movie. Close ‘holes’ using the function imfill with the option ‘holes’. What are the pros and cons of using the close morphological operation?

4. Perform a watershed transform to split off adjoining cells. a. Use the function bwperim to outline cell bodies on all 7 images shown above and blur the edges using a Gaussian filter size 5 with standard deviation.

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