Partial-Volume Bayesian Classification of Material Mixtures in MR Volume Data using Voxel Histograms

Abstract

We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Because we allow for mixtures of materials and treat voxels as regions, our technique reduces the classification artifacts that thresholding can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make more-accurate volume measurements and classifies noisy, low-resolution data well. There are two unusual aspects to our approach. First, we assume that, due to partial-volume effects, voxels can contain more than one material, e.g., both muscle and fat; we compute the relative proportion of each material in the voxels. Second, we incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, p(x), from the samples and then looking at the distribution of values that p takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that we classify is chosen to match the spacing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.

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