Probabilistic intuitionistic fuzzy c-means algorithm with spatial constraint for human brain MRI segmentation

Segmentation of brain MRI images becomes a challenging task due to spatially distributed noise and uncertainty present between boundaries of soft tissues. In this work, we have presented intuitionistic fuzzy set theory based probabilistic intuitionistic fuzzy c-means with spatial neighborhood information method for MRI image segmentation. We have investigated two well known negation functions namely, Sugeno’s negation function and Yager’s negation function for representing the image in terms of intuitionistic fuzzy sets. The proposed approach takes leverage of intuitionistic fuzzy set theory to address vagueness and uncertainty present in the data. The spatial neighborhood information term in the segmentation process is included to dampen the effect of noise. The segmentation performance of the proposed method is evaluated in terms of average segmentation accuracy and Dice score. Further, the comparison of the proposed method with other similar state-of-art methods is carried out on two publicly available brain MRI dataset which shows the significant improvements in segmentation performance in terms of average segmentation accuracy and Dice score. The proposed approach achieves on average 91% average segmentation accuracy in the presence of noise and intensity inhomogeneity on BrainWeb simulated dataset, which outperformed the state-of-art methods.