Semantic of Automatically Generated Interval-Valued Memberships Functions in Brain Magnetic Resonance Images

Medical image segmentation plays a crucial role in diagnosis assistance. In previous works, we proposed a classification method called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC), which generates Interval-Valued Membership Functions (IVMF) and fuzzy predicates. They can be analyzed to interpret the images. In this work, a methodology is proposed to study the semantic of IVMF generated from brain MRI as input of the T2-LFPC. It is possible to understand both membership functions and predicates by visual inspecting positions and shapes of the IVMF. Some changes are applied on the images. Transformations include: zero mean additive noise, contrast-stretching and brightness increase and decrease. Changes in the images by transformations are reflected in the histograms of the pixels belonging to white matter, gray matter, and cerebrospinal fluid, in the IVMF and the values of their measures. Therefore, as changes are reflected in the IVMF as expected, the methodology proposed here could be considered suitable for image analysis.