# Mean values from spherical ROIs in 3D image I am trying to measure the mean pixel values from ~ 100 individual ROIs in a 4D image hyperstacks (x,y,z,and 4 channels) and doing this for a few hundred images. I have a list of xyz coords to site the centre of the ROI. Now, I know how to do this for a 2D image with multiple channels, but I'm wondering if there is a simple way to do this in 3D? As a further detail, I would ideally like the ROI to be "spherical" not a cube. I'd like to be able to vary the radius of the spherical ROI to optimise the measurements. Any help would be much appreciated. My current thought is to loop through each location, seed a spherical ROI wave (in binary, not sure how), multiply the image by this ROIwave and sum the pixels and divide by ROI size to get the result. I've not tried this yet, but it sound like it could be pretty slow and given the optimisation and number of images, I'm wondering about a more efficient method. Maybe ImageStats can use a 3D ROI wave...?
It is not clear from your description whether the hyper-stack uses uniformly-gridded xyz coordinates. Assuming this is the case, it should easy to calculate the 3D spherical ROI intercept on each z-plane, giving a 2D circular ROI over x and y. Then perform the 2D analyses on each z-plane, and aggregate the results.
s.r.chinn wrote:
It is not clear from your description whether the hyper-stack uses uniformly-gridded xyz coordinates. Assuming this is the case, it should easy to calculate the 3D spherical ROI intercept on each z-plane, giving a 2D circular ROI over x and y. Then perform the 2D analyses on each z-plane, and aggregate the results.
Thanks. They are uniformly gridded. x=y in scaling but z is bigger. I will try out this idea but somehow need to deal with that difference in scaling...
I think the z-scaling will be the least of your problems. The 3D ROI is defined by (x-x0)^2+(y-y0)^2+(z-z0)^2 = R^2, where {x0, y0, z0} are the ROI center. The 2D ROI will be given by the boundary relation (x-x0)^2+(y-y0)^2 = R^2-(DimOffset(waveName, 2)+r*DimDelta(waveName, 2 )-z0)^2, where 'r' is the wave point index for the 2D calculation plane, and DimDelta(waveName, 2) is the scaling increment for 'z', in the same dimensional units as 'x' and 'y'. Note that DimOffset(waveName, 2) stands for a fictitious 'leftz' and DimDelta(waveName, 2) stands for a fictitious 'delz'.
I'd like to offer a completely different tool that may work for this application: The FPClustering operation. This could work if the ROIs are distinct. Your ROI's will be clusters and with the appropriate choice of flags (and normalizing the large dimension using /NOR) the /CM flag should give you a measure of the mean. A.G. WaveMetrics, Inc.
@A.G. I have had a look at FPClustering I'm not sure it will do what I want. I re-read my question and I think what I wrote might be misleading. As I understand it, the /CM flag will give the center of mass for a cluster of coords, i.e. location. For my application, the list of coordinates I have are independent and I want to sample the mean pixel value at each location (for each channel). I don't want to take the single pixel value at each location or a cubic ROI centered on the coordinate, I'd like to specify a tiny sphere (voxelated approximation) and get the mean pixel value in this ROI. I think ImageAnalyzeParticles would work if I had cubic voxels. @s.r.chinn thanks for this. I'll give it a try.
sjr51 wrote:
@A.G. I have had a look at FPClustering I'm not sure it will do what I want. I re-read my question and I think what I wrote might be misleading. As I understand it, the /CM flag will give the center of mass for a cluster of coords, i.e. location. For my application, the list of coordinates I have are independent and I want to sample the mean pixel value at each location (for each channel). I don't want to take the single pixel value at each location or a cubic ROI centered on the coordinate, I'd like to specify a tiny sphere (voxelated approximation) and get the mean pixel value in this ROI. I think ImageAnalyzeParticles would work if I had cubic voxels.
Thanks for the explanation. In that case, if you can use box instead of sphere neighborhood, you can use MatrixOP as follows: Let x0,y0,z0 be the center of the box. Define deltaZ (in layers) that corresponds to half the height of the box. Then loop over layers from z0-deltaZ to z0+deltaZ. In each iteration extract a rectangular region around (x0,y0) using MatrixOp subRange() function and sum it. The rest should be straightforward.
Variable rs=x0-deltaX
Variable re=x0+deltaX
// similarly for cs and ce.
MatrixOP/O aa=sum(subrange(w,rs,re,cs,ce))
I hope this helps, AG