Preprocessing methods to apply sobel edges detection

Question

I am currently writing a segmentation algorithm in Matlab based on the distance transform and watershed lines to analyse my data (Cell Microscopy, see picture 1 for the raw image). The part of separating cells works quite well, but I have troubles with the first part of the algorithm : the preprocessing and the edges detection parts.

Link towards the images : http://postimg.org/gallery/20pf5za9q/be769288/

Indeed, my segmentation is based on bright field images with natural contours quite well highlighted, but due to some noise illumination from nucleus, the contour of some cells if often truncated when I apply the edges detection method (Sobel Filter). For instance, the edges detected in image 2 lead to a good segmentation (image 3), but the next frame could have the same cell with an intern illumination leading to a bad edges detection (image 4) and thus a bad segmentation (image 5).

I would like to find either a edges detection method more sensitive, or a method allowing to diminish the importance of the cells intern illumination. As I am far to be an expert in Image processing algorithms, I do not know if what I want can be simple to get, or If I need to look at other strategies.

Thank you very much for your time and advices.

Answer 1

You could also "play" with the different settings and thresholds in the edge method in Matlab. I had the same problem a while ago and it was solved choosing the "log' method (Laplassian of Gaussian).

You could begin by sticking to the Sobel method and changing the threshold (see http://www.mathworks.com/help/images/ref/edge.html?refresh=true for details), then proceed to experimenting with the other methods to find if any of them suits you better than the sobel. I would also check matlab's boundary tracing function ( http://www.mathworks.com/help/images/ref/bwboundaries.html ) perhaps with noholes enabled (I think this would eliminate the nucleus area appearing as an extra object and messing your segmentation). Hope this helps a bit. :D