Open Access   Article

Brain Tumor Detection Using Clustering Method

R. Dhatchayini1 , K. Mohamed Amanullah2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 50-57, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.5057

Online published on Sep 30, 2018

Copyright © R. Dhatchayini, K. Mohamed Amanullah . 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.

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Citation

IEEE Style Citation: R. Dhatchayini, K. Mohamed Amanullah, “Brain Tumor Detection Using Clustering Method”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.50-57, 2018.

MLA Style Citation: R. Dhatchayini, K. Mohamed Amanullah "Brain Tumor Detection Using Clustering Method." International Journal of Computer Sciences and Engineering 6.9 (2018): 50-57.

APA Style Citation: R. Dhatchayini, K. Mohamed Amanullah, (2018). Brain Tumor Detection Using Clustering Method. International Journal of Computer Sciences and Engineering, 6(9), 50-57.

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Abstract

In this paper, an algorithm about brain tumor detection using the K- means clustering and graphcut technique that uses the color based segmentation method to track tumor objects in magnetic resonance (MR) brain images.Magnetic resonance imaging (MRI) is a advanced medical imaging technique giving rich information about the human soft tissue anatomy.Magnetic Resonance Imaging has become a widely used method of high quality medical imaging..Tumor is an uncontrolled development of tissues in any part of the body. Brain tumor is intrinsically genuine and lifethreatening. Immense quantities of passings have been checked because of the reality of incorrect recognition. Brain tumor detection in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the correct size, shape, boundary extraction and area of tumor. A comparative study on clustering with K-Means algorithm and graphcut algorithm was also done with the MRI image dataset using MATLAB.

Key-Words / Index Term

Brain Tumor,Clustering,K-means,Magnetic Resonance Imaging (MRI),Thresholding, Histogram-Based method, Graphcut

References

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