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Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm

1
Department of Information and Communication Engineering, Islamic University, Kushtia 7003, Bangladesh
2
Department of Information and Communication Engineering, Noakhali Science & Technology University, Sonapur 3814, Noakhali, Bangladesh
3
Department of Biomedical Engineering, Islamic University, Kushtia 7003, Bangladesh
4
DiSTA, University of Insubriaz, 21100 Varese, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Discipline of Information Technology, National University of Ireland Galway, H91 TK33 Galway, Ireland.
Big Data Cogn. Comput. 2019, 3(2), 27; https://doi.org/10.3390/bdcc3020027
Received: 3 April 2019 / Revised: 28 April 2019 / Accepted: 30 April 2019 / Published: 13 May 2019
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Abstract

In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms. View Full-Text
Keywords: magnetic resonance imaging; T-means clustering; fuzzy C-means clustering; template-based K-means and modified fuzzy C-means (TKFCM); feature extraction; gray level intensity magnetic resonance imaging; T-means clustering; fuzzy C-means clustering; template-based K-means and modified fuzzy C-means (TKFCM); feature extraction; gray level intensity
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Alam, M.S.; Rahman, M.M.; Hossain, M.A.; Islam, M.K.; Ahmed, K.M.; Ahmed, K.T.; Singh, B.C.; Miah, M.S. Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm. Big Data Cogn. Comput. 2019, 3, 27.

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