Spatial intuitionistic fuzzy set based image segmentation

Author(s): Deepika Koundal*, Bhisham Sharma & Ekta Gandotra

Segmentation of images is one of the most challenging tasks because of restricted observation of the specialists and uncertainties presented in medical knowledge. Crisp values are inadequate to model real situation due to imprecise information frequently used in decision making process. Various intuitive methods have been explored to understand the ambiguity and uncertainty of medical images to carry out segmentation task. Therefore, in this paper, an attempt has been made to segment the medical images using clustering method based on Intuitionistic fuzzy set. With the incorporation of spatial information into intuitionistic clustering named as Spatial Intuitionistic Fuzzy C Means (SIFCM), the object of interest is segmented more accurately and effectively. The benefits of incorporating spatial information is that it is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information as well as capable of reduction of noisy spots and spurious blobs. The performances of proposed methods are evaluated for real images. The results indicate that SIFCM is more effective, and noise tolerant as compared with the fuzzy c-means clustering.

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