ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Enhanced K-Means by Using Grey Wolf Optimizer for Brain MRI Segmentation


Affiliations
1 Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, Indonesia
     

   Subscribe/Renew Journal


Segmentation is an essential part of the detection and classification series. The best result of brain MRI detection was followed by the best segmentation process. Supporting brain MRI detection accurately, one of the ways could be used by increasing segmentation. This paper utilizes one of the segmentation methods which is called clustering. We propose a clustering approach using K-Means. K-Means has advantages easy to understand, fast process, and guarantees convergence. But it has drawbacks which are initialization cluster center randomly, sometimes it is given good results but sometimes it is not. Therefore, this research proposes to optimize the weak side of K-Means using a grey wolf optimizer. Initialization cluster center was chosen based on fitness value. The fitness value of this paper is Sum Square Error (SSE), we purpose to minimize the SSE of the population and searching new positions depend on Gray Wolf Optimization (GWO)’s rule. The final position of GWO would be initialized by K-Means. The series of our research steps are acquisition image, grayscaling, resizing, segmentation, and analysis performance based on MSE and PSNR. The best result of the purposed method is k=17 which PSNR (16.09) and MSE (15.99). GWO K-Means were given the best outcome segmentation brain MRI based on measuring error value and PSNR.

Keywords

Gray Wolf Optimization, K-Means, MRI Segmentation, Sum Square Error.
Subscription Login to verify subscription
User
Notifications
Font Size

  • A. Jijja and D. Rai, “Efficient MRI Segmentation and Detection of Brain Tumor using Convolutional Neural Network”, International Journal of Advanced Computer Science and Applications, Vol. 10, No. 4, pp. 536–541, 2019.

  • B. Vaishnavee and K. Amshakala, “Study of Techniques used for Medical Image Segmentation Based on SOM”, International Journal of Soft Computing and Engineering, Vol. 4, No. 4, pp. 40-44, 2014.

  • K. M. Iftekharuddin, “Techniques in Fractal Analysis and their Applications in Brain MRI”, Proceedings of International Conference on Medical Imaging System Technology, pp. 63-86, 2005.

  • L. Liu, L. Kuang and Y. Ji, “Multimodal MRI Brain Tumor Image Segmentation using Sparse Subspace Clustering Algorithm”, Computational and Mathematical Methods in Medicine, Vol. 2020, pp. 1-13, 2020.

  • S. Roy and S.K. Bandyopadhyay, “A New Method of Brain Tissues Segmentation from MRI with Accuracy Estimation”, Procedia Computer Science, Vol. 85, No. 3, pp. 362-369, 2016.

  • N.M. Zaitoun and M.J. Aqel, “Survey on Image Segmentation Techniques”, Procedia Computer Science, Vol. 65, No. 2, pp. 797-806, 2015.

  • M. Babrdel Bonab, S.Z. Mohd Hashim, N.E.N. Bazin and A.K.Z. Alsaedi, “An Effective Hybrid of Bees Algorithm and Differential Evolution Algorithm in Data Clustering”, Mathematical Problems in Engineering, Vol. 2015, pp. 1-17, 2015.

  • E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui and J. Long, “A Survey of Clustering with Deep Learning: From the Perspective of Network Architecture”, IEEE Access, Vol. 6, pp. 39501-39514, 2018.

  • E.A. Pambudi, P.N. Andono and R.A. Pramunendar, “Image Segmentation Analysis Based on K-Means PSO by using Three Distance Measures”, ICTACT Journal on Image and Video Processing, Vol. 9, No. 1, pp. 1821-1826, 2018.

  • H. Kundra and J. Kaur, “Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques”, International Journal of Computer Science and Network Security, Vol. 9, No. 10, pp. 132-142, 2009.

  • R. Dash and R. Dash, “Comparative Analysis of K-Means And Genetic Algorithm Based Data Clustering”, International Journal of Advanced Computer and Mathematical Sciences, Vol. 3, No. 2, pp. 257-265, 2012.

  • U.R. Raval and C. Jani, “Implementing and Improvisation of K-means Clustering Algorithm”, International Journal of Computer Science and Mobile Computing, Vol. 5, No. 5, pp. 191-203, 2016.

  • T.A. Rashid, D.K. Abbas and Y.K. Turel, “A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer”, PLoS One, Vol. 14, No. 3, pp. 1-23, 2019.

  • S. Mirjalili, S.M. Mirjalili and A. Lewis, “Grey Wolf Optimizer”, Advances in Engineering Software, Vol. 69, pp. 46-61, 2014.

  • R. Rayaroth and G. Sivaradje, “Grey Wolf Optimization based Sensor Placement for Leakage Detection in Water Distribution System”, International Journal of Recent Technology and Engineering, Vol. 7, No. 5, pp. 180-188, 2019.

  • A. Wosiak, A. Zamecznik and K. Niewiadomska Jarosik, “Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types”, Proceedings of Federated Conference on Computer Science and Information Systems, pp. 323-329, 2016.

  • O. Ertenlice and C.B. Kalayci, “A Survey of Swarm Intelligence for Portfolio Optimization: Algorithms and Applications”, Swarm and Evolutionary Computation, Vol. 39, pp. 36-52, 2018.

  • R. Cheng and Y. Jin, “A Social Learning Particle Swarm Optimization Algorithm for Scalable Optimization”, Information Sciences, Vol. 291, No. 3, pp. 43-60, 2015.

  • L. Rosenberg and G. Willcox, “Artificial Swarm Intelligence The Technology of Artificial Swarm Intelligence (ASI) has been Shown to Amplify”, Proceedings of Annual Information Technology, Electronics and Mobile Communication, pp. 1-18, 2018.

  • S. Dhankhar, “Brain MRI Segmentation using K- means Algorithm”, Proceedings of National Conference on Advances in Knowledge Management, pp. 1-5, 2010.

  • R.M. Pinki, “Estimation of the Image Quality under Different Distortions”, International Journal of Engineering and Computer Science, Vol. 5, No. 1, pp. 17291-17296, 2016.


Abstract Views: 170

PDF Views: 1




  • Enhanced K-Means by Using Grey Wolf Optimizer for Brain MRI Segmentation

Abstract Views: 170  |  PDF Views: 1

Authors

Elindra Ambar Pambudi
Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, Indonesia
Abid Yanuar Badharudin
Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, Indonesia
Agung Purwo Wicaksono
Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, Indonesia

Abstract


Segmentation is an essential part of the detection and classification series. The best result of brain MRI detection was followed by the best segmentation process. Supporting brain MRI detection accurately, one of the ways could be used by increasing segmentation. This paper utilizes one of the segmentation methods which is called clustering. We propose a clustering approach using K-Means. K-Means has advantages easy to understand, fast process, and guarantees convergence. But it has drawbacks which are initialization cluster center randomly, sometimes it is given good results but sometimes it is not. Therefore, this research proposes to optimize the weak side of K-Means using a grey wolf optimizer. Initialization cluster center was chosen based on fitness value. The fitness value of this paper is Sum Square Error (SSE), we purpose to minimize the SSE of the population and searching new positions depend on Gray Wolf Optimization (GWO)’s rule. The final position of GWO would be initialized by K-Means. The series of our research steps are acquisition image, grayscaling, resizing, segmentation, and analysis performance based on MSE and PSNR. The best result of the purposed method is k=17 which PSNR (16.09) and MSE (15.99). GWO K-Means were given the best outcome segmentation brain MRI based on measuring error value and PSNR.

Keywords


Gray Wolf Optimization, K-Means, MRI Segmentation, Sum Square Error.

References