Robustness of an adaptive MRI segmentation algorithm parametric intensity inhomogeneity modeling

We propose an unsupervised segmentation algorithm for magnetic resonance images (MRI) endowed with a parametric intensity inhomogeneity (IIH) correction schema and the on-line estimation of the image model intensity class means. The paper includes an extensive experimentation that shows that the algorithm is robust in the sense that it converges to good image segmentations despite the initial estimation of the image model intensity class means. The algorithm is, therefore, highly automatic requiring no interactive tuning to obtain good image segmentations, an appealing property in clinical environments. The IIH field and intensity class means estimation consists of the gradient descent of the restoration error of the intensity corrected image. Our algorithm does not work on the logarithmic transformation of the image, thus allowing for the explicit distinction between the smooth multiplicative field and the independent and identically distributed additive noise at each image voxel.