Evaluating intensity normalization on MRIs of human brain with multiple sclerosis

Intensity normalization is an important pre-processing step in the study and analysis of Magnetic Resonance Images (MRI) of human brains. As most parametric supervised automatic image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. One of the fast and accurate approaches proposed for intensity normalization is that of Nyul and colleagues. In this work, we present, for the first time, an extensive validation of this approach in real clinical domain where even after intensity inhomogeneity correction that accounts for scanner-specific artifacts, the MRI volumes can be affected from variations such as data heterogeneity resulting from multi-site multi-scanner acquisitions, the presence of multiple sclerosis (MS) lesions and the stage of disease progression in the brain. Using the distributional divergence criteria, we evaluate the effectiveness of the normalization in rendering, under the distributional assumptions of segmentation approaches, intensities that are more homogenous for the same tissue type while simultaneously resulting in better tissue type separation. We also demonstrate the advantage of the decile based piece-wise linear approach on the task of MS lesion segmentation against a linear normalization approach over three image segmentation algorithms: a standard Bayesian classifier, an outlier detection based approach and a Bayesian classifier with Markov Random Field (MRF) based post-processing. Finally, to demonstrate the independence of the effectiveness of normalization from the complexity of segmentation algorithm, we evaluate the Nyul method against the linear normalization on Bayesian algorithms of increasing complexity including a standard Bayesian classifier with Maximum Likelihood parameter estimation and a Bayesian classifier with integrated data priors, in addition to the above Bayesian classifier with MRF based post-processing to smooth the posteriors. In all relevant cases, the observed results are verified for statistical relevance using significance tests.

The paper extensively validates, for the first time, the two-stage intensity normalization method proposed by Nyul et al. (2000) on multi-site multi-scanner Magnetic Resonance Images (MRIs) of human brains of patients with Multiple Sclerosis (MS) at varying progression stages. The approach is shown to be effective in rendering intensities that are more homogenous for the same tissue type while simultaneously resulting in better tissue type separation. The deciles-based approach also results in improved performance of image segmentation algorithms, independent of the algorithms’ complexities, on the task of MS lesion identification against a linear normalization approach.Figure optionsDownload high-quality image (110 K)Download as PowerPoint slideResearch highlights
► Evaluate Intensity normalization of Nyul et al. (2000).
► On multi-site, multi-scanner MRIs.
► In presence of multiple sclerosis (MS) lesions and varying stages of disease progression.
► Assess effect on MS lesion segmentation against linear normalization.
► Over various image segmentation algorithms.