Image segmentation using spatial intuitionistic fuzzy C means clustering

• Published in: 2014 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 5
• Main Authors: Tripathy, B. K., Basu, Avik, Govel, Sahil
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Clustering algorithms; Fuzzy C Means Clustering; Image segmentation; Indexes; Intuitionistic Fuzzy C Means; Intuitionistic Fuzzy set; Magnetic resonance imaging; Noise; Noise measurement; Spatial Fuzzy C Means
• ISBN: 1479939749; 9781479939749
• DOI: 10.1109/ICCIC.2014.7238446

A fuzzy algorithm is presented for image segmentation of 2D gray scale images whose quality have been degraded by various kinds of noise. Traditional Fuzzy C Means (FCM) algorithm is very sensitive to noise and does not give good results. To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information. The spatial function is the sum of all the membership functions within the neighborhood of the pixel under consideration. The results showed that this approach was not as sensitive to noise as compared to the traditional FCM algorithm and yielded better results. The algorithm we have proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM). Intuitionistic refers to the degree of hesitation that arises as a consequence of lack of information and knowledge. Proposed method is comparatively less hampered by noise and performs better than existing algorithms.