Consistent segmentation using a Rician classifier

Several popular classification algorithms used to segment magnetic resonance brain images assume that the image intensities, or log-transformed intensities, satisfy a finite Gaussian mixture model. In these methods, the parameters of the mixture model are estimated and the posterior probabilities for each tissue class are used directly as soft segmentations or combined to form a hard segmentation. It is suggested and shown in this paper that a Rician mixture model fits the observed data better than a Gaussian model. Accordingly, a Rician mixture model is formulated and used within an expectation maximization (EM) framework to yield a new tissue classification algorithm called Rician Classifier using EM (RiCE). It is shown using both simulated and real data that RiCE yields comparable or better performance to that of algorithms based on the finite Gaussian mixture model. As well, we show that RiCE yields more consistent segmentation results when used on images of the same individual acquired with different T1-weighted pulse sequences. Therefore, RiCE has the potential to stabilize segmentation results in brain studies involving heterogeneous acquisition sources as is typically found in both multi-center and longitudinal studies.

Figure optionsDownload high-quality image (59 K)Download as PowerPoint slideHighlights
► Noise in magnetic resonance images should be modeled as Rician distribution.
► Rician distribution fits the MR image histogram better than a Gaussian one.
► Cortical surfaces from the brain MR images can be better delineated using Rician models in a segmentation algorithm compared to a Gaussian one.
► Segmentations between same brain MR images acquired under different pulse sequences are more consistent using Rician modeling.