Magnetic Resonance Imaging (MRI) is an important paraclinical tool for diagnosing Multiple Sclerosis (MS) and providing several markers of disease activity and evolution. Traditionally, hypointense lesions on T1-weighted images have been reported to represent areas where demyelination and axonal loss have occurred, and are the images usually selected for segmenting the encephalic parenchyma. Based on the fact that in T1-weighted images MS lesions cannot be located within cerebrospinal fluid regions (CSF), a correct detection of such regions is very useful to filter MS’s false detections. However, the gray levels similarity among some MS lesions and CDF regions makes of the encephalic parenchyma detection process a difficult task. In this work we propose an approach for detecting CSF regions in which, for taking into consideration aforementioned gray-level vagueness, as well as the intrinsic uncertainty of CSF boundaries, we make use of fuzzy techniques. The proposed algorithm performs a fuzzy local analysis based on gray-level and texture characteristics, but considering the location and size of the CSF regions. As a result, the algorithm allows discriminating cerebrospinal fluid regions inside the intracranial region, providing confidence degrees that match with the possibility of including pixels associated to MS lesion