Rendering a 3D Volume from a stack of 2D images

I have seen a number of questions related to the title.  I even experienced the question myself.  Here, i would like to document my approach.

The Question


If you are in the medical imaging field, at the time of writing, you are almost guaranteed that you'd need to reconstruct a 3D volume based on a stack of 2D images(let it be a stack of 'gray-scale' images).  The question is how in Matlab?


The Possible Solutions

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Part 0 - Assumptions

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• all 2D images are of the same dimension, hence your 3D volume can hold all of them in a rectangular cube
• majority of the pixels in each of the 2D images have 3D spatial relationships (you can't visualize much if the pixels in each of the 2D images are of some random distribution. )

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Part 1 - Visualizing 3D Volume from A Stack of 2D Images

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To visualize or reconstruct a 3D volume from a stack of 2D images, you can try the following toolkits in matlab.

[1] 3D CT/MRI images interactive sliding viewerhttp://www.mathworks.com/matlabcentral/fileexchange/29134-3d-ctmri-images-interactive-sliding-viewer

[2] Viewer3D http://www.mathworks.com/matlabcentral/fileexchange/21993-viewer3d

[3] Image3 http://www.mathworks.com/matlabcentral/fileexchange/21881-image3

[4] Surface2Volume http://www.mathworks.com/matlabcentral/fileexchange/8772-surface2volume

[5] SliceOMatic http://www.mathworks.com/matlabcentral/fileexchange/764

Note that if you are familiar with VTK, you can try this: [6] matVTKhttp://www.cir.meduniwien.ac.at/matvtk/

I am currently sticking with [5] SliceOMatic for its simplicity and ease of use. However, by default, rendering 3D is quite slow in Matlab. Turning on openGL would give faster rendering. (http://www.mathworks.com/help/techdoc/ref/opengl.html) Or simply put, set(gcf, 'Renderer', 'OpenGL').

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Part 2 - Interpolating pixels in between the slices

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To interpolate pixels in between the slices, you need to specify an interpolation method (some of the above toolkits have this capability / flexibility. Otherwise, to give you a head start, some examples for interpolation are bicubic, spline, polynomial etc..(you can work this out by looking up on google or google/scholar for interpolation methods much more specific to your problem domain).

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Part 3 - 3D Pre-processing

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Looking at the procedures, one may process the volumetric data by processing each of the 2D images first. In many advanced algorithms, or in true 3D processing, what you can do is to process the volumetric data in 3D domain first (simply put, you take the 26 neighbors or more in to account first.). Once this step is done, you can simply output the volumetric data into a stack of 2D images for cross-sectional viewing or supply to one of the aforementioned toolkits for 3D viewing or output to third party 3D viewing applications.

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I have followed the above concepts for my own medical imaging research projects.

The above writing is documented here but was aiming at answering this question on StackOverflow.