FAST-PVE: Extremely Fast Markov Random Field Based Brain MRI Tissue Classification

Abstract

Abstract. We present an extremely fast method named FAST-PVE for tissue classification and partial volume estimation of 3-D brain magnetic resonance images (MRI) using a Markov Random Field (MRF) based spatial prior. The tissue classification problem is central to most brain MRI analysis pipelines and therefore solving it accurately and fast is important. The FAST-PVE method is experimentally confirmed to tissue classify a standard MR image in under 10 seconds with the quantitative accuracy similar to other state of art methods. A key component of the FAST-PVE method is the fast ICM algorithm, which is generally applicable to any MRF-based segmentation method, and formally proven to produce the same segmentation result as the standard ICM algorithm.

Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review

“…The discretization-based methods then try to solve maximally probable PVCs from this discretized set resorting MRF approaches to model spatial interaction between adjacent voxels [32][33][34] . While the restriction to a discrete set of PVC values is perfectly reasonable given the noisiness of the images, the discretization approaches are usually very time consuming, especially when compared to fast two step approaches [25,28] .…”

“…The idea is to integrate out the variable wi1describing the percentage of tissue type 1 in a voxel by numerical integration. Note that with current computers the numerical integration does not present computational problem and can be solved very fast [28] . Advantages of this more complicated probabilistic approach over the two simple approaches include possibility to include spatial regularization in the form of MRFs to the step 1 [25,26] and the applicability to multispectral images [26] .…”

“…The use of the MRF requires the user to set a proper weighting parameter for the prior which may be considered as a disadvantage [8] . However, often quoted disadvantage of the added computational cost (e.g., the reference [8] ) of the MRF, can be overcome by new rapid algorithms capable of performing MRF based segmentation of the typical 3-D MR images within few seconds [28] . While the two-step algorithms often use spatial MRF prior during the partial volume classification step, they typically do not utilize spatial information during the second, PVC estimation, step.…”

“…The technical problem in the probabilistic partial volume classification approach is the construction of the probability models for mixed tissue classes; the class conditional densities for pure tissue classes are modelled by the normal density. The probability densities for the mixed tissue types can be constructed based on a marginalization technique developed originally in references [19,20] and further applied in references [25][26][27][28] . The idea is to integrate out the variable wi1describing the percentage of tissue type 1 in a voxel by numerical integration.…”

“…[28]. Note how the manual hard labeling completely misses the CSF in the interhemispheric fissure as well as in the superior frontal sulcus pointed by red arrows.…”