Contrast stretching for brain magnetic resonance imaging

We propose a technique to enhance the accuracy of the Bayesian classifier for frontal lobe T1 - weighted images. In order to overcome the difference in intensity between different objects, we conducted experiments on the discrimination of likelihood and the relation between the upper error limit and the classification accuracy of contrast based stretching. The Naive Bayesian classifier was used for verification in consideration of the low prior probability and the final experiment using the cross validation method of the approximate model, showing three classes (gray matter, white matter, cerebrospinal fluid). The purpose of this study is to provide a preliminary study to assist in the analysis of lesions and patterns of frontal lobe MR imaging.