Manual segmentation of medical images is unpractical because it is time consuming, not reproducible, and
prone to human error. It is also very difficult to take into account the 3D nature of the images. Thus, semi- or
fully-automatic methods are of great interest. Current segmentation algorithms based on an Expectation-
Maximization (EM) procedure present some limitations. The algorithm by Ashburner et al., 2005, does not
allow multichannel inputs, e.g. two MR images of different contrast, and does not use spatial constraints between
adjacent voxels, e.g. Markov random field (MRF) constraints. The solution of Van Leemput et al., 1999, employs
a simplified model (mixture coefficients are not estimated and only one Gaussian is used by tissue class, with
three for the image background). We have thus implemented an algorithm that combines the features of these
two approaches: multichannel inputs, intensity bias correction, multi-Gaussian histogram model, and Markov
random field (MRF) constraints. Our proposed method classifies tissues in three iterative main stages by way of
a Generalized-EM (GEM) algorithm: (1) estimation of the Gaussian parameters modeling the histogram of the
images, (2) correction of image intensity non-uniformity, and (3) modification of prior classification knowledge
by MRF techniques. The goal of the GEM algorithm is to maximize the log-likelihood across the classes and
voxels. Our segmentation algorithm was validated on synthetic data (with the Dice metric criterion) and real
data (by a neurosurgeon) and compared to the original algorithms by Ashburner et al. and Van Leemput et al.
Our combined approach leads to more robust and accurate segmentation.
This study looks into the rigid-body registration of pre-operative anatomical high field and interventional low field magnetic resonance images (MRI). The accurate 3D registration of these modalities is required to enhance the content of interventional images with anatomical (CT, high field MRI, DTI), functional (DWI, fMRI, PWI), metabolic (PET) or angiography (CTA, MRA) pre-operative images. The specific design of the interventional MRI scanner used in the present study, a PoleStar N20, induces image artifacts, such as ellipsoidal masking and intensity inhomogeneities, which affect registration performance. On MRI data from eleven patients, who underwent resection of a brain tumor, we quantitatively evaluated the effects of artifacts in the image registration process based on a normalized mutual information (NMI) metric criterion. The results show that the quality of alignment of pre-operative anatomical and interventional images strongly depends on pre-processing carried out prior to registration. The registration results scored the highest in visual evaluation only if intensity variations and masking were considered in image registration. We conclude that the alignment of anatomical high field MRI and PoleStar interventional images is the most accurate when the PoleStar's induced image artifacts are corrected for before registration.