Department of Computer Science
University of Cyprus


A problem that occurs in quantitative texture analysis of magnetic resonance imaging (MRI) 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 prior to further image analysis. The objective of this work was to investigate six previously described MRI intensity normalization methods and propose the most appropriate for the pre-processing of brain T2-weighted MR images acquired from 38 symptomatic untreated multiple sclerosis (MS) subjects. The following normalization methods were investigated: Contrast Stretch Normalization (CSN), Intensity Scaling (IS), Histogram Stretching (HS), Histogram Normalization (HN), Gaussian Kernel Normalization (GKN), and Histogram Equalization (HE). The main findings of this study can be summarized as follows: 1) Lesion texture features were affected differently by the normalization process for both the 0 and 6-12 months MRI scans. For example, for the features median and contrast there was significant difference between 0 and 6-12 months for the original MRI images but not for the HN normalized ones. On the other hand for the feature complexity there was no significant difference between 0 and 6-12 months for the original MRI images, but there was for the HN normalized images. 2) The statistical lesion feature analysis between the original and the normalized images showed that the HN method gave the highest number of significant features after normalization for both the 0 and 6-12 months MRI scans. 3) The HN normalization method gave the best performance compared to the other normalization methods with respect to the distance measures, structural similarity index, coefficient of variation, and correlation coefficient between the original and the normalized 0 and 6-12 months MRI scans. Thus, based on the above findings it is recommended that the simple HN normalization method could be used prior to quantitative texture analysis in the case study presented. The findings of this study provide evidence that texture features of MRI-detectable brain white matter lesions may have an additional potential role in the clinical evaluation of MRI images in MS. This is a prerequisite step in the assessment of texture features as surrogate markers of disease progression. However, a larger scale study is needed to establish the application in clinical practice and for computing shape parameters and texture features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

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