Bhasker Dappuri,Suman Mishra,N. Lakshmi Devi,




Magnetic resonance imaging,Brain tumor,Thresholding,Fuzzy C-means,K-means,Hybrid clustering,


Segmentation of MR brain image is quite useful in detection of tumors and further diagnosis. However, precise segmentation of tumors plays a significant role in diagnosing the patient more effectively. Previously, there are plenty of approaches was implemented and however they were failed to detect the exact tumor which led to the failure diagnosis. Therefore, an accurate detection of tumor is required for effective diagnosis. Here, this article presented an efficient segmentation of MR brain image tumors. Our approach includes a hybrid clustering mechanism with pre-processed by savitzky-golay filter (SGF). In addition, tumor area also estimated for better diagnosis of patient. Simulation results disclosed the superiority of proposed hybrid approach over conventional segmentation algorithms in terms of computational complexity and segmentation accuracy.


I. A.M. Usó, F. Pla and P.G. Sevila, “Unsupervised Image Segmentation
Using a Hierarchical Clustering Selection Process”, Structural,
Syntactic, and Statistical Pattern Recognition, vol. 4109, pp. 799-807,
II. A. R. Barakbah and Y.Kiyoki. “A Pillar algorithm for K-means
Optimization by Distance Maximization for Initial Centroid
Designation”, IEEE Symposium on Computational Intelligence and
Data Mining, pp. 61-68, 2009.
III. A.Z. Arifin and A. Asano, “Image segmentation by histogram
thresholding using hierarchical cluster analysis”, Pattern Recognition
Letters, vol. 27, no. 13, pp. 1515-1521, 2006.
IV. A. Sehgal, et. al, “Automatic Brain Tumor Segmentation and
Extraction in MR Images”, In Proc. of Inter. Conf. on Adv. in
Sig.Proces., Pune, India, pp. 104-107, 2016.
V. E. A. Maksoud, M.Elmogy and R.A. Awadhi, “Brain Tumor
Segmentation based on a Hybrid Clustering Technique”, Egyptian
Informatics Journal, vol. 16, no. 1, 2015.
VI. H. P. A. Tjahyaningtijas, “Brain Tumor image segmentation in MRI
images”, IOP Conf. Series: Materials Science and Engineering, vol.
336, 012012, 2018.
VII. http://www.unitconversion.org/typography/millimeters-to-pixels-xconversion.
VIII. J. E. A. L. Kostka, “A review of the medical image segmentation
algorithms”, In: Peng SL., Dey N., Bundele M. (eds) Computing and
Network Sustainability, Lecture Notes in Networks and Systems, vol
75, Springer, Singapore, May 2019.
IX. J. Selvakumar, A. Lakshmi and T. Arivoli, “Brain Tumor segmentation
and its area Calculation in Brain MR images using K-means Clustering
and Fuzzy C-means algorithm”, International Conference on Advances
in Engineering, Science and Management,pp. 186-190, 2012.
X. M.H. F.Zarandia, M. Zarinbala and M. Izadi, “Systematic image
processing for diagnosing brain tumors: A Type-II fuzzy expert system
approach”, Applied soft computing, pp. 285-294, 2011.
XI. N. Dhanachandra, K. Maglem and Y. J. Chanu, “Image segmentation
using K-means and subtractive clustering algorithm”, Procedia
Computer Science, vol 54, pp. 764-771, 2015.
XII. T. Shen and Y. Wang, “Medical image segmentation based on
improved watershed algorithm”, In: Proc. of 3rd Advanced Information
J. Mech. Cont.& Math. Sci., Vol.-15, No.-1, January (2020) pp 182-191
Copyright reserved © J. Mech. Cont.& Math. Sci.
Bhasker Dappuri et al
Technology, Electronic and Automation Control, Chongqing, China,
IEEE, Oct. 2018.
XIII. T. W. Chen, Y.-L. Chen and S.-Y.Chien, “Fast Image Segmentation
Based on K-Means Clustering with Histograms in HSV Color Space”,
Journal of Scientific Research, vol. 44, no.2, pp.337-351, 2010.
XIV. Z. Beevi and M.Sathik, “An effective approach for segmentation of
MRI images: combining spatial information with fuzzy c-means
clustering”, European Journal of Scientific Research, vol. 41, no.3,
pp.437-451, 2010.

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