Bias Correction of Multiple MRI Images Based on an Improved Nonparametric Maximum Likelihood Method

With the wide application of nuclear magnetic resonance imaging, the multiplicative bias field in nuclear magnetic resonance images has created great difficulties for doctors in reading diagnostics and for computers in autoprocessing. Most previous methods eliminate the bias field in the image by estimating a single unknown bias field. An improved method that uses the nonparametric maximum likelihood to jointly eliminate bias from multiple magnetic resonance imaging (MRI) images is proposed in this paper. The method uses the statistics from the same location across different patient images, rather than within an image, and builds a “multiresolution” nonparametric tissue model conditioned on image location. We use a separate and nonparametric model to consider the intensity values at each pixel and utilize nonparametric maximum likelihood distance measures to simultaneously eliminate the bias of magnetic resonance (MR) images from different patients. Finally, the performance of the same was tested on a synthetic MRI dataset and a real MRI dataset and is found that the proposed algorithm provides better performance than the method of using entropy minimization across images and the most popular and widely used method, N4.