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).
@article{arxiv.2002.01985,
title = {3DPIFCM Novel Algorithm for Segmentation of Noisy Brain MRI Images},
author = {Arie Agranonik and Maya Herman and Mark Last},
journal= {arXiv preprint arXiv:2002.01985},
year = {2020}
}