Fuzzy Clustering Algorithms for Effective Medical Image Segmentation

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Deepali Aneja 1,* Tarun Kumar Rawat 1

1. Department of Electronics and Communication Engineering, Netaji Subas Institute of Technology, New Delhi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2013.11.06

Received: 15 Feb. 2013 / Revised: 3 Jun. 2013 / Accepted: 20 Aug. 2013 / Published: 8 Oct. 2013

Index Terms

Fuzzy Clustering, Fuzzy C-Means, FCM Type-II, Intuitionistic FCM, Fuzzy Set


Medical image segmentation demands a segmentation algorithm which works against noise. The most popular algorithm used in image segmentation is Fuzzy C-Means clustering. It uses only intensity values for clustering which makes it highly sensitive to noise. The comparison of the three fundamental image segmentation methods based on fuzzy logic namely Fuzzy C-Means (FCM), Intuitionistic Fuzzy C-Means (IFCM), and Type-II Fuzzy C-Means (T2FCM) is presented in this paper. These algorithms are executed in two scenarios– both in the absence and in the presence of noise and on two kinds of images– Bacteria and CT scan brain image. In the bacteria image, clustering differentiates the bacteria from the background and in the brain CT scan image, clustering is used to identify the abnormality region. Performance is analyzed on the basis cluster validity functions, execution time and convergence rate. Misclassification error is also calculated for brain image analysis.

Cite This Paper

Deepali Aneja, Tarun Kumar Rawat, "Fuzzy Clustering Algorithms for Effective Medical Image Segmentation", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.11, pp.55-61, 2013. DOI:10.5815/ijisa.2013.11.06


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