Abstract:
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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 |