An Accurate and Efficient Bayesian Method for Automatic Segmentation of Brain MRI

Abstract

Automatic 3D segmentation of the brain from MR scans is a challenging problem that has received enormous amount of attention lately. Of the techniques reported in literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3D segmentation procedure for brain MR scans. It has several salient features namely, (1) instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. This may be a more realistic model and avoids the need for a logarithmic transformation. (2) A brain atlas is used in conjunction with a robust registration procedure to find a nonrigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from non-brain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. (3) Finally, a novel algorithm is presented which is a variant of the EM procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods.

Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

“…(1). Intensive research effort has been attempted to correct this artifact [3,12,14,20,[28][29][30]. For a recent review, see [31] In this study two IIH correction methods, the H3 [32] and the N3 [28] method, are tested.…”

“…Basically, context-based methods constrain the solution with the image spatial information, and can yield a smoother segmentation map. Examples include random field modeling [9][10][11][12][13][14], regularized FCM clustering [15]. With the spatial coherence assumption, the performance of context-based methods is generally more stable and more reliable.…”

Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy

“…However this model can only be used on images from the same modality as it assumes similar intensity values between images. In Marroquin et al (2002), Vemuri et al (2000), a level-set based image registration algorithm was introduced that was designed to non-rigidly register two 3D volumes from the same modality of imaging. This algorithm was computationally efficient and was used to achieve atlas-based segmentation.…”

Fast medical image mixture density clustering segmentation using stratification sampling and kernel density estimation

“…Due to the presence of noise, intrinsic tissue variation, partial volume effects, unclear tissue boundaries and intensity non-uniformity, medical image segmentation remains a challenging task [2]. There are a lot of methods available for medical image segmentation [3,4], such as specific probability density distribution [5], decision tree [6] and neural networks [7]. In the wide range of segmentation methods, clustering algorithms are termed unsupervised classification methods that organize unlabeled feature vectors into clusters or "natural groups" so that the samples within a cluster are more similar to each other than the samples belonging to different clusters [8].…”