Spatial Rough Intuitionistic Fuzzy C-Means Clustering for MRI Segmentation

Medical image segmentation is the challenging problem in real-time applications due to the occurrence of noise and uncertainties between different tissues in the magnetic resonance images (MRI). To overcome the issue, spatial rough intuitionistic fuzzy C-means method has been proposed. The segmentation of MR brain image has been implemented by updating the MRI based on global spatial information of pixel with intuitionistic fuzzy c-means algorithm for segmenting cerebro spinal fluid, white matter and gray matter tissues. Intuitionistic fuzzy sets and rough sets have been used to deal with uncertainty and vagueness in medical images. Intuitionistic fuzzy sets are used for image representations by using non-membership value, hesitation along with the membership value for the MR image. The membership value and non-membership value have been obtained using fuzzy hexagonal membership and fuzzy complement function respectively. Further, roughness measures are done to determine the initial cluster centroids by considering lower and upper approximation and the fuzzy c-means clustering algorithm has been updates by the euclidean distance between the pixels based on global spatial information for segmenting MR brain image. The proposed method have been implemented and analysed with quantitative and qualitatively for the synthetic and real MR images. Experimental results exhibit a higher degree of segmentation accuracy on both synthetic and real MR images compared to existing methods and achieves better performance. [GRAPHICS] .

Automated summary

The proposed method improves segmentation accuracy of MRI images by utilizing intuitionistic fuzzy sets and rough sets to deal with uncertainty and vagueness in medical images.