What is it about?
Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.
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Why is it important?
Extended work with clustering segmentation
Perspectives
Chiranji Lal Chowdhary joins VIT University as an Assistant Professor (Senior) in the School of Information Technology and Engineering. Prior to coming to VIT, he was a Lecturer at the M.S. Ramaiah Institute of Technology, Bangalore. Chiranji Lal received his B.E. in Computer Science and Engineering from M.B.M. Engineering College, Jai Narain Vyas University Jodhpur and his M.Tech. from the Visvesvaraya Technological University, Belagavi (M.S. Ramaiah Institute of Technology, Bangalore). Chiranji Lal’s teaching interests include artificial intelligence, digital image processing, c. c++ and soft computing. He has won the PG level project award in 2008. His primary research interests are in the field of image processing and computational intelligence. He has published 10 research papers in reputed journals and conferences at national and international level. Mr. Chowdhary is a Life Member of the Indian Society for Technical Education (ISTE), Computer Society of India (CSI), and Indian Science Congress Association (ISCA).
Chiranji Lal Mr Chowdhary
VIT University
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This page is a summary of: Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images, Journal of Biomimetics Biomaterials and Biomedical Engineering, January 2017, Trans Tech Publications,
DOI: 10.4028/www.scientific.net/jbbbe.30.12.
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