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Noise Adaptive FCM algorithm for Segmentation of MRI Brain Images


Author : Harmanpreet Singh and Ramandeep Kaur

Pages : 916-921, DOI: https://doi.org/10.14741/ijcet/v.8.4.4
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Abstract

Image segmentation is an important task in many image processing applications. Fuzzy C means algorithm has been widely used for the segmentation. There are many versions of the traditional FCM algorithm which uses the local spatial information to increase the robustness of this algorithm in presence of noise, but all these algorithms do not successfully segment the images contaminated by heavy noise. In order to solve this problem, non-local spatial information present in the image is utilized. The filtering parameter ‘h’ in the non-local information is a crucial parameter which needs to be appropriately determined, irrespective of using a single constant value of ‘h’; we can determine its value using the standard deviation of noise present in the image. The adaptive non-local information determined is termed as noise adaptive non-local spatial information. This adaptive non-local information is used in the FCM algorithm for the segmentation of MRI images. In this paper Noise adaptive FCM algorithm (NAFCM) using adaptive non-local information is proposed. Therefore the proposed algorithm utilizes adaptive non-local information making it robust in presence of noise as well as preserving the details present in the image. The efficiency of the proposed algorithm is demonstrated by validation studies on synthetic as well as simulated brain MRI images. The results of the proposed algorithm show that the proposed algorithm is robust to noise and as compared to other state of the art algorithms.

Keywords: Image segmentation, Fuzzy c-means, Magnetic Resonance image, non-local spatial, Rician noise

Article published in International Journal of Current Engineering and Technology, Vol.8, No.4 (July/Aug 2018)

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