Contour completion in image segmentation

Question

I am attempting to use machine learning (namely random forests) for image segmentation. The classifier utilizes a number of different pixel level features to classify pixels as either edge pixels or non edge pixels. I recently applied my classifier to a set of images that are pretty difficult to segment even manually (Image segmentation based on edge pixel map) and am still working on obtaining reasonable contours from the resulting probability map. I also applied the classifier to an easier set of images and am obtaining quite good predicted outlines (Rand index > 0.97) when I adjust the threshold to 0.95. I am interested in improving the segmentation result by filtering contours extracted from the probability map.

Here is the original image:

The expert outlines:

The probability map generated from my classifier:

This can be further refined when I convert the image to binary based on a threshold of 0.95:

I tried filling holes in the probability map, but that left me with a lot of noise and sometimes merged nearby cells. I also tried contour finding in openCV but this didn't work either as many of these contours are not completely connected - a few pixels will be missing here and there in the outlines.

Edit: I ended up using Canny edge detection on the probability map.

Answer 1

The initial image seems to be well contrasted and I guess we can simply threshold to obtain a good estimate of the cells. Here is a morphological area based filtering of the thresholded image:

Threshold:

Area based opening filter(this needs to be set based on your dataset of cells under study):

Area based closing filter(this needs to be set based on your dataset of cells under study):

Contours using I-Erosion(I):

Code snippet:

C is input image
C10 = C>10; %threshold depends on the average contrast in your dataset
C10_areaopen = bwareaopen(C10,2500); %area filters average remove small components that are not cells
C10_areaopenclose = ~bwareaopen(~C10_areaopen,100); %area filter fills holes 
se = strel('disk',1);
figure, imshow(C10_areaopenclose-imerode(C10_areaopenclose,se)) %inner contour

To get smoother shapes I guess fine opening operations can be performed on the filtered images, thus removing any concave parts of the cells. Also for cells that are attached one could use the distance function and the watershed over the distance function to obtain segmentations of the cells: http://www.ias-iss.org/ojs/IAS/article/viewFile/862/765

I guess this can be also used on your probability/confidence maps to perform nonlinear area based filtering.