Brain MR image normalization in texture analysis of multiple sclerosis

Abstract

A problem that occurs in texture analysis and quantitative analysis of magnetic resonance imaging (MRI), is that the extracted results are not comparable between consecutive or repeated scans or, within the same scan, between different anatomic regions. The reason is that there are intra-scan and inter-scan image intensity variations due to the MRI instrumentation. Therefore, image intensity normalization methods should be applied to magnetic resonance (MR) images prior to further image analysis. The objective of this work was to investigate six different MRI intensity normalization methods and propose the most appropriate for the pre-processing of brain T2-weighted MR images acquired from 22 symptomatic untreated multiple sclerosis (MS) subjects and 10 healthy volunteers. Following image normalization, texture analysis was carried out in original and normalized images for normal appearing white matter (NAMW) and MS lesions, detected in transverse T2weighted MR images. The best normalization method (Histogram Normalization (HN)) demonstrated a smaller Kullback Leibler divergence (0.05, 0.06) suggesting appropriateness for pre-processing MR images used in texture analysis of MS brain lesions. This is a prerequisite step in the assessment of texture features as surrogate markers of disease progression.