4

I'm looking to construct a 3-D surface of a part of the brain based on 2-D contours from cross-sectional slices from multiple angles. Once I get this shape, I want to "fit" it to another set of contours via rescaling.

I'm aspiring to do this in the context of an MCMC analysis (So as to be able to make inferences, so it would be very nice if I could easily compute the volume of the rescaled surface, and the minimum distance between a given point of the distance. (Accurate approximations are fine).

What would be a good image reconstruction algorithm that allows for volume and distance to be quickly calculated?

DavidShor
  • 1,281
  • 1
  • 11
  • 18
  • you mean a a good 3D image reconstruction algorithm? maybe you only need an algorithm to estimate 3-D connex region? something like level set segmentation? – robin girard Aug 19 '10 at 06:47
  • Just a good image reconstruction algorithm(The regions involved are often not convex). It's just that there are a lot of different ones, and I'm not familiar which ones are best in the context I'm approaching the problem in. (Easy to parametrize estimated surfaces, easy to compute volume, and easy to find minimum distance from a given point) – DavidShor Aug 19 '10 at 21:55

1 Answers1

2

I tend to use Gaussian process models for this and similar surface estimation (Possible relevant examples here and here). But perhaps your question would be best asked over on Stack Overflow? Could you provide more details on your input data (contours from a surface model of MRI data?) as well as your desired outputs: scale parameter which minimizes L2 distance between two sets of contours? Volume? Distance? Which distance?

The answer will probably also be specific to the programming language you're working with because, for example, I believe OpenGL has a built in function for determining the minimum distance from a point to the surface (possible example?).

Community
  • 1
M. Tibbits
  • 1,693
  • 14
  • 25