LOCAL HISTOGRAM CORRECTION OF MRI SPATIALLY DEPENDENT IMAGE PIXEL INTENSITY NONUNIFORMITY

We describe a computationally straightforward post-hoc statistical met hod of correcting spatially dependent image pixel intensity nonuniform ity based on differences in local tissue intensity distributions. Pixe l intensity domains for the various tissues of the composite image are identified and compared to the distributions of local samples, The no nuniformity correction is calculated as the difference of the local sa mple median from the composite sample median for the tissue class most represented by the sample. The median was chosen to reduce the effect ers on determining the sample statistic and to allow a sample size sm all enough to accurately estimate the spatial variance of the image in tensity nonuniformity,The method was designed for application to two-d imensional images, Simulations were used to estimate optimal condition s of local histogram kernel size and to test the accuracy of the metho d under known spatially dependent nonuniformities, The method was also applied to correct a phantom image and cerebral MRIs from 15 healthy subjects. Results show that the method accurately models simulated spa tially dependent image intensity differences. Further analysis of clin ical MR data showed that the variance of pixel intensities within the cerebral MRI slices and the variance of slice volumes within individua ls were significantly reduced after nonuniformity correction. Improved brain-cerebrospinal fluid segmentation was also obtained. The method significantly reduced the variance of slice volumes within individuals , whether it was applied to the native images or images edited to remo ve nonbrain tissues. This statistical method was well behaved under th e assumptions and the images tested. The general utility of the method was not determined, but conditions for testing the method under a var iety of imaging sequences is discussed. We believe that this algorithm can serve as a method for improving MR image segmentation for clinica l and research applications.