The segmentation of brain tissue in magnetic resonance imaging (MRI) plays an important role in clinical analysis
and is useful for many applications including studying brain diseases, surgical planning and computer assisted
diagnoses. In general, accurate tissue segmentation is a difficult task, not only because of the complicated
structure of the brain and the anatomical variability between subjects, but also because of the presence of noise
and low tissue contrasts in the MRI images, especially in neonatal brain images.
Fuzzy clustering techniques have been widely used in automated image segmentation. However, since the
standard fuzzy c-means (FCM) clustering algorithm does not consider any spatial information, it is highly sensitive
to noise. In this paper, we present an extension of the FCM algorithm to overcome this drawback, by
combining information from both T1-weighted (T1-w) and T2-weighted (T2-w) MRI scans and by incorporating
spatial information. This new spatially constrained FCM (SCFCM) clustering algorithm preserves the homogeneity
of the regions better than existing FCM techniques, which often have difficulties when tissues have
overlapping intensity profiles.
The performance of the proposed algorithm is tested on simulated and real adult MR brain images with
different noise levels, as well as on neonatal MR brain images with the gestational age of 39 weeks. Experimental
quantitative and qualitative segmentation results show that the proposed method is effective and more robust
to noise than other FCM-based methods. Also, SCFCM appears as a very promising tool for complex and noisy
image segmentation of the neonatal brain.