A Threshold-based Brain Tumour Segmentation from MR Images using Multi-Objective Particle Swarm Optimization
محورهای موضوعی : Pattern Recognition
Katkoori Arun Kumar
1
(Research Scholar, KLEF, Hyderabad, India)
Ravi Boda
2
(Associate Professor, KLEF, Hyderabad, India)
کلید واژه: Multi-Objective Optimization, PSO, Median Filter, Threshold, Image Segmentation,
چکیده مقاله :
The Pareto optimal solution is unique in single objective Particle Swarm Optimization (SO-PSO) problems as the emphasis is on the variable space of the decision. A multi-objective-based optimization technique called Multi-Objective Particle Swarm Optimization (MO-PSO) is introduced in this paper for image segmentation. The multi-objective Particle Swarm Optimization (MO-PSO) technique extends the principle of optimization by facilitating simultaneous optimization of single objectives. It is used in solving various image processing problems like image segmentation, image enhancement, etc. This technique is used to detect the tumour of the human brain on MR images. To get the threshold, the suggested algorithm uses two fitness(objective) functions- Image entropy and Image variance. These two objective functions are distinct from each other and are simultaneously optimized to create a sequence of pareto-optimal solutions. The global best (Gbest) obtained from MO-PSO is treated as threshold. The MO-PSO technique tested on various MRI images provides its efficiency with experimental findings. In terms of “best, worst, mean, median, standard deviation” parameters, the MO-PSO technique is also contrasted with the existing Single-objective PSO (SO-PSO) technique. Experimental results show that Multi Objective-PSO is 28% advanced than SO-PSO for ‘best’ parameter with reference to image entropy function and 92% accuracy than Single Objective-PSO with reference to image variance function.
The Pareto optimal solution is unique in single objective Particle Swarm Optimization (SO-PSO) problems as the emphasis is on the variable space of the decision. A multi-objective-based optimization technique called Multi-Objective Particle Swarm Optimization (MO-PSO) is introduced in this paper for image segmentation. The multi-objective Particle Swarm Optimization (MO-PSO) technique extends the principle of optimization by facilitating simultaneous optimization of single objectives. It is used in solving various image processing problems like image segmentation, image enhancement, etc. This technique is used to detect the tumour of the human brain on MR images. To get the threshold, the suggested algorithm uses two fitness(objective) functions- Image entropy and Image variance. These two objective functions are distinct from each other and are simultaneously optimized to create a sequence of pareto-optimal solutions. The global best (Gbest) obtained from MO-PSO is treated as threshold. The MO-PSO technique tested on various MRI images provides its efficiency with experimental findings. In terms of “best, worst, mean, median, standard deviation” parameters, the MO-PSO technique is also contrasted with the existing Single-objective PSO (SO-PSO) technique. Experimental results show that Multi Objective-PSO is 28% advanced than SO-PSO for ‘best’ parameter with reference to image entropy function and 92% accuracy than Single Objective-PSO with reference to image variance function.
[1] Linda G. Shapiro and George C. Stockman: “Computer Vision”., New Jersey, Prentice-Hall, 2001, pp 279-325.
[2] R.O Duda and P.E. Hart, " Pattern Classification and Scene Analysis ", John Wiley & Sons, New-York, 1973, vol. 3, pp 731-749.
[3] N. Papamarkos, B. Gatos, “A new approach for multilevel threshold Selection”. Graphics Models Image Processing, 1994, vol 56, issue 5, pp 357-370.
[4] S. Boukharouba, J.M. Rebordao, P.L. Wendel, “An amplitude segmentation method based on the distribution function of an image”. Computer Vision Graphics Image Processing, 1985, vol. 29 issue 1, pp 47-59.
[5] Y.-T. Kao, E. Zahara and I-W. Kao, “A Hybridized Approach to Data Clustering,” Expert Systems with Applications, 2008, Vol. 34, No. 3, pp.1754-1762.
[6] “The essential guide to brain tumors”, National brain tumor society (NBTS), 2007.
[7] Molka DHIEB, Sabeur Masmoudi, Mohamed Ben Messa OUD, and Faten Ben Arfia “2-D Entropy Image Segmentation on Thresholding Based on Particle Swarm Optimization (PSO)”, in 1st International Conference on Advanced Technologies for Signal and Image processing (ATSIP), 2014, pp. 143-147, doi: 10.1109/ATSIP.2014.6834594.
[8] Bo Chen , Qing-hua Zou, Yan Li “A new image segmentation model with local statistical characters based on variance minimization”, Applied Mathematical Modelling, 2014, vol. 39, issue 12, pp 3227-3235.
[9] Mala, C., Sridevi, M. “Multilevel threshold selection for image segmentation using soft computing techniques”, Soft Computing, 2016, vol. 20, issue 5, pp 1793–1810.
[10] H. Zhang, J. Fritts, and S. Goldman, “An Entropy-based objective evaluation method for image segmentation”, in Proc. SPIE-Storage and Retrieval Methods and Application for Watershed Transform Multimedia, 2004, pp 38-49.
[11] Horst K. Hahn et al,“ The Skull stripping Problem in MRI solved by a Single 3D”, Proc. MICCAI,LNCS, Springer, Berlin, 2000, pp 134-143.
[12] Habba Maryam, Ameur Mustapha and Jabrane Younes , “A multilevel thresholding method for image segmentation based on multiobjective particle swarm optimization”, International Conference on wireless technologies, Embedded and Intelligent systems (WITS), 2017, pp. 1-6, doi: 10.1109/WITS.2017.7934620.
[13] Pham TX, Siarry P, Oulhadj H. “A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.” Magnetic Resonance Imaging, 2019, vol 61, pp 41-65.
[14] Coello Coello CA, González Brambila S, Figueroa Gamboa J, Castillo Tapia MG, Hernández Gómez R, “Evolutionary multiobjective optimization: open research areas and some challenges lying ahead”, Complex & Intelligent Systems, 2020, vol.6, pp 221-236.
[15] Hu W, Yen GG, “Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system”, IEEE Transactions on Evolutionary Computation, 2015, vol 19, issue 1, pp 1–18.
[16] S. B. Emami, N. Nourafza, S. Fekri–Ershad, "A method for diagnosing of Alzheimer's disease using the brain emotional learning algorithm and wavelet feature", Journal of Intelligent Procedures in Electrical Technology, vol. 13, no. 52, pp. 1-15, 2021.
[17] G. Mahesh Kumar, K. Arun Kumar, P. Rajashekar Reddy, J. Tarun Kumar, “A Novel Approach of Tumor Detection in Brain using MRI Scan Images”, Research J. Pharm. and Tech., 2020, vol 13, issue 12, pp 5914-5918.
