Deepali Aneja

Work place: Department of Electronics and Communication Engineering, Netaji Subas Institute of Technology, New Delhi, India



Research Interests: Image Compression, Image Manipulation, Image Processing


Deepali Aneja completed her B. Tech. in 2008. She is pursuing her Masters of Technology in Signal Processing (ECE) from Netaji Subas Institute of Technology, New Delhi. Currently, she is working on her Masters Dissertation and this paper is the implementation of her thesis work. Her research interests are Image Segmentation and Signal Processing.

Author Articles
Fuzzy Clustering Algorithms for Effective Medical Image Segmentation

By Deepali Aneja Tarun Kumar Rawat

DOI:, Pub. Date: 8 Oct. 2013

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.

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