"Sven" <sven.holcombe@gmail.deleteme.com> wrote in message <gro7to$k0a$1@fred.mathworks.com>...
> trancemissionxxi@gmail.com wrote in message <11b5e1edfdcf4eeaa9d5f7c49b221c40@a7g2000yqk.googlegroups.com>...
> > Sven,
> >
> > For some reason fspecial3 is not showing up on file exchange anymore.
> > I googled it and the link is:
> >
> > http://www.mathworks.com/matlabcentral/files/15981/fspecial3.m
> >
> > I do 3D cell nuclei tracking, which are almost spherical, but I know
> > about the acetabular issue from papers I read on hough transform
> > (tried using it before for my problem).
> >
> > Indeed the fft can become computationally intensive if the image and
> > mask are large (for me largest volume element is 49x49x49 pixels).
> > I like your idea on doing the convolution on the hough transformed
> > circles/disks. Since you are done with using the grayscale and have
> > binary data for the slice circles, why not resize the image and do
> > convolution on smaller image size (if large matrices are a problem).
> > Alternatively, you can use a scanning box algorithm on your new voxel
> > volume with detected disks.
> >
> > There's also this paper (not sure if you have access):
> > http://dx.doi.org/10.1016/j.patrec.2005.11.019
> >
> > Cheers.
>
> Thanks Trance, that paper actually helped quite a lot. It essentially has an implementation of my "find the best sphere to fit a set of planar circles" problem, and it ends up boiling down to pretty simple trigonometry after you make the (fairly reasonable) assumption that the circles all have the same (x,y) location.
>
> I had a little attempt at the fspecial3 approach, but as you know it is a little computationally expensive, and I'm quite happy with the results so far from the hough circles approach. The CT scans are often higher than 49x49 pixels across the area of interest without downsampling, so hough circles were much faster to compute than the full fft.
>
> One further question to do with the next step of what I'm considering. Do you know or use any kind of radial filtering? I'm wondering if I could somehow filter the image volume after I find the femoral head using the head centre and radius, in a way that would highlight the acetabular surface. I'm pretty sure a simple threshold outside the sphere would do a reasonable job, but I'd just like to know if what I'm thinking is effective and maybe even commonly used.
>
Just a little update if anyone's interested.
I found that the above radial filter worked quite well in my application. Let me explain:
The hough circle detection algorithm I was using was "houghcircles" from the file exchange. By default, this file uses a 'canny' filter on a given image. A minor problem I was having was that the femoral head and acetabular are in close proximity, and it was difficult to distinguish the edge of one from the edge of the other.
So, I went for a 2 phase approach.
First phase:
 Simple circle detection (as per houghcircles) in order to find a *pretty good* [x0,y0] location of the centre of my sphere. (the paper linked above has some tips)
Second phase:
 A second round of hough circle detection, however this time I don't use the raw input image. Instead, something like the following:
% xVoxels,yVoxels : vectors for x and y pixel locations
% im : raw image at a given height (size matches yVoxels/xVoxels)
[xPx, yPx] = meshgrid(xVoxels, yVoxels); % Cartesian coords of pixel locs
[th,rho] = cart2pol(xPxx0, yPxy0);
[px,py] = gradient(im,xVoxels,yVoxels); % XY Gradient filter
pr = sin(th).*py + cos(th).*px; % Radial gradient filter
bw_filt = bwmorph(pr<50, 'skel')
This gives a thin line at the boundaries where the gradient of the image is negative 50 or lower (in my case, this fits well to the gradient of bone to nonbone).
I then provide this bw_filt image instead of the raw image to the houghcircles function (and I modified the function so it didn't use a canny filter on this image). The result was a much improved detection of the outer femoral head surface, since it effectively went from detecting:
femoral head inner edge
femoral head outer edge
acetabular outer edge
acetabular inner edge
to just femoral head outer edge and acetabular inner edge.
Anyway, just thought that might come in handy.
Cheers,
Sven.
