It is still hard to deal with artifacts in magnetic resonance images (MRIs), particularly when the latter are to be segmented. This paper introduces a novel feature, namely the spatial entropy of intensity that allows a pattern-based representation which enhances the MRI segmentation despite presence of high levels of noise and intensity non uniformity (INU) within MRI data. Moreover, we bring out that ensembles of classifiers used with the proposed feature have significantly enhanced structured MRI segmentation. Thus, to conduct experiments, MRIs with different artifact levels were extracted and exploited from the Brain Web MRI database. The obtained results reveal that the proposed feature, especially when used with ensembles of classifiers has significantly enhanced the overall MRI segmentation.
Ensemble Classification Using Entropy-Based Features for MRI Tissue Segmentation