  We present a novel algorithm named 3DPIFCM, for automatic segmentation of
noisy MRI Brain images. The algorithm is an extension of a well-known IFCM
(Improved Fuzzy C-Means) algorithm. It performs fuzzy segmentation and
introduces a fitness function that is affected by proximity of the voxels and
by the color intensity in 3D images. The 3DPIFCM algorithm uses PSO (Particle
Swarm Optimization) in order to optimize the fitness function. In addition, the
3DPIFCM uses 3D features of near voxels to better adjust the noisy artifacts.
In our experiments, we evaluate 3DPIFCM on T1 Brainweb dataset with noise
levels ranging from 1% to 20% and on a synthetic dataset with ground truth both
in 3D. The analysis of the segmentation results shows a significant improvement
in the segmentation quality of up to 28% compared to two generic variants in
noisy images and up to 60% when compared to the original FCM (Fuzzy C-Means).
