Identifying the most suitable histogram normalization technique for machine learning based segmentation of multispectral brain MRI data

The main drawback of magnetic resonance imaging (MRI) represents the lack of a standard intensity scale. All observed numerical values are relative and can only be interpreted together with their context. Before feeding MRI data volumes to supervised learning segmentation procedures, their histograms need to be registered to each other, or in other words, they need a so-called normalization. The most popular histogram normalization technique used to assist brain MRI segmentation is the algorithm proposed by Nyuĺ et al in 2000, which aligns the histograms of a batch of MRI volumes without paying attention to possible focal lesions that might distort the histogram. Alternately, some recent works applied histogram normalization based on a simple linear transform, and reported achieving slightly better accuracy with them. This paper proposes to investigate, which is the most suitable method and parameter settings for histogram normalization to be performed before the segmentation of brain MRI images, separately in the cases of absence and presence of focal lesions.