An Automated Brain Tumor Detection in MRI using Firefly Optimized Segmentation

Prabhjot Kaur, Amardeep Kaur

Abstract


In the medical field brain tumor detection is an important application. The existing techniques of segmentation has various limitations. Existing techniques ignored the medical images which have poor quality or low brightness. Segmentation becomes the challenging issue as the image contains non-uniform object texture, cluttered objects, different image content and image noise. New technique of segmentation is proposed by research to detect tumor from MR images using firefly algorithm, then tumor is segmented and its features are extracted from MR image.  The main goal of Research to design an algorithm for MRI based brain tumor segmentation using firefly algorithm and to improve the accuracy of the tumor detection. Fireflies produce a reaction in their body which produce light , this chemical reaction is called bioluminescent. By using firefly technique it is possible to detect and localize tumor accurately. For comparative analysis, various parameters are used to demonstrate the superiority of proposed method over the conventional ones.

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References


. Srikanta Patnaik, "Automated Brain Tumor Segmentation and Detection in MRI using Enhanced Darwinian Particle Swarm Optimization(EDPSO)", 2nd International Conference on Intelligent Computing, Communication & Convergence, vol: 92, 2016, pp: 475-480

. P. Dvorak, K. Bartusek, W. G. Kropatsch, Z. Smekal, "Automated Multi-Contrast Brain Pathological Area Extraction from 2D MR Images", Journal of Applied Research and Technology, vol: 13, 2015, pp: 58-69

. Sushmit Ghosh, Soham Kundu, Sushovan Chowdhury, AurpanMajumder, "Optimal Statistical Structure Validation of Brain Tumors Using Refractive Index", 3rd International Conference on Recent Trends in Computing, vol: 57, 2015, pp: 168-177

. Asra Aslam, Ekram Khan, M.M. Sufyan Beg, "Improved Edge Detection Algorithm for Brain Tumor Segmentation", Second International Symposium on Computer Vision and the Internet, vol: 58, 2015, pp: 430-437

. J. Mehena, M. C. Adhikary, "Brain Tumor Segmentation and Extraction of MR Images Based on Improved Watershed Transform", IOSR Journal of Computer Engineering, e-ISSN: 2278-0661,p-ISSN: 2278-8727, vol: 17, issue 1, Feb 2015, pp: 1-5

. Sahil J Prajapati, Kalpesh R Jadhav, "Brain Tumor Detection By Various Image Segmentation Techniques With Introducation To Non Negative Matrix Factorization", International Journal of Advanced Research in Computer and Communication Engineering, ISSN (Online) 2278-1021, ISSN (Print) 2319-5940, vol. 4, issue 3, March 2015, pp: 599-603

. Yash Sharma, MeghaChhabra, "An Improved Automatic Brain Tumor Detection System", International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, vol: 5, issue 4, 2015, pp: 11-15

. Simran Arora, Gurjit Singh, "A Study of Brain Tumor Detection Techniques", International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, vol: 5, issue 5, May 2015, pp: 1272-1278

. Brundha B, Nagendra Kumar M, "MR Image Segmentation of brain to detect brain tumor and its area calculation using K-Means clustering and Fuzzy C-Means algorithm", International Journal For Technological Research In Engineering, ISSN (Online): 2347 - 4718, vol: 2, issue 9, May-2015, pp: 1781-1785

. Eman Abdel-Maksoud, Mohammed Elmogy, Rashid Al-Awadi, "Brain tumor segmentation based on a hybrid clustering technique", Egyptian Informatics Journal, ISSN: 1110-8665, Vol: 16, 2015, pp: 71-81

. H. Kaur and D. R. Sharma, “A Survey on Techniques for Brain Tumor Segmentation from Mri,” IOSR J. Electron. Commun. Eng., vol. 11, no. 5, pp. 01–05, 2016.


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