A Novel Approach to Brain Tumor Detection Using Texture Based Gabor Filter Followed by Genetic Algorithm
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It is very important to consider the factors like blurred boundaries and external noise while analyzing the brain tumor structures. It is difficult to segment accurately the brain MRI. Many studies in both developing and developed countries indicate that an inactive diagnosis has led to the death of the majority of people who suffer from brain tumor. The proposed novel method is a filter mechanism based on texture Gabor filter, which is followed by a genetic algorithm proposed to improve segmentation accuracy. A texture-based Gabor filter has been used to detect irregularities and to extract statistical properties further used in segmentation and classification. In order to improve segmentation effectiveness, a better separation of different clusters of the features from Gabor filter is studied. An objective function has been also formalized to adjust filter parameters with gradient descent and genetic algorithm. This document has demonstrated the effects of the segmentation of both qualitative and quantitative productivity. The findings show that the novel proposed approach works better with respect to the segmentation accuracy.
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Upendra Kumar. Significant Enhancement of Segmentation Efficiency of Retinal Images Using Texture-Based Gabor Filter Approach Followed by Optimization Algorithm. International Journal of Computer Vision and Image Processing. 7. 44-58, 2017.
P.D. Sathya, R. Kalyani, V.P. Sakthivel, Color image segmentation using Kapur, Otsu and Minimum Cross Entropy functions based on Exchange Market Algorithm, Expert Systems with Applications, Volume 172, 2021, 114636, ISSN 0957-4174.
Gonzales and Woods, Digital Image Processing, 2nd Edition, Pearson, 2002.
Nordin, P., and Banzhaf, W. Programmatic com-pression of images and sound. In Genetic Pro-gramming 1996, Proceedings of the First Annual Conference, Koza J. R. et al. editors, pp. 345-350, MIT Press, 1996.
G. Rajesh Chandra, Kolasani Ramchand H. Rao, Tumor Detection In Brain Using Genetic Algo-rithm, Procedia Computer Science, Volume 79, 2016, Pages 449-457, ISSN 1877-0509.
N. Goel, Dr. A. Yadav, and Dr. B.M. Singh, Medical Image Processing: A Review IEEE International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH) , 57-62, 2016.
Koma'rudin, N., Zakaria, Z., Soh, P., Lago, H., Alsariera, H., Hassan, N., A Review of Recent Microwave Breast Imaging, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (4), pp. 257-267.
Identifying Bone Cancer Using Markov Random Field Segmentation, (4 June, 2018) Available online:
K. K. Gupta, N. Dhanda, U. Kumar, A Novel Hybrid Method for Segmentation and Analysis of Brain MRI for Tumor Diagnosis, Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 16-27 (2020).
Huo, Jing & Okada, Kazunori & van Rikxoort, Eva & Kim, Grace Hyun & R Alger, Jeffry & B Pope, Whitney & Goldin, Jonathan & S Brown, Matthew Ensemble segmentation for GBM brain tumors on MR images using confidence-based averaging. Medical physics, 2013. 40. 093502. 10.1118/1.4817475.
Alexander Zotin, Konstantin Simonov, Mikhail Kurako,Yousif Hamad, Svetlana Kirillova, Edge detection in MRI brain tumor images based on fuzzy C-means clustering, 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, PP-1262-1270, 2018.
A. Shenbagarajan, V. Ramalingam, C. Balasubramanian, and S. Palanivel, Tumor Diagnosis in MRI Brain Image using ACM Segmentation and ANN-LM Classification Techniques, Indian Journal of Science and Technology, Vol 9(1), 1-12, Jan 2016.
Zhe Zhang and Jianhua Song, " A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model, Recent Advances on Signal Processing and Deep Learning for Public Security and Engineering Applications, Appl. Sci., 9(7), 1332, March 2019.
Fangfang Han, Bin Liu, Junchao Zhu, and Baofeng Zhang, Algorithm Design for Edge De-tection of High-Speed Moving Target Image un-der Noisy Environment, Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data, Sensors, 19(2), 343, Jan 2019.
Pinzon-Arenas, J., Jimenez-Moreno, R., Hernandez-Beleno, R., Face Completion Using Semantic Segmentation and Geometric Features, (2018) International Review of Automatic Control (IREACO), 11 (6), pp. 304-313.
Omar, H., Zaky, E., Ibrahim, G., Elsawy, A., An Algorithm Based Levenberg Marquardt Method with Genetic Algorithm for Solving Continuation Problems, (2020) New Trends in Nonlinear Analysis and Applications, 1 (2), pp. 85-99.
Chong Zhang, Xuanjing Shen, Hang Cheng, and Qingji Qian, Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations, Hindawi, International Journal of Bio-medical Imaging, Volume 2019, Article ID 7305832, 11 pages.
Kale Vaishnaw, Vandana B. Malode, A Novel Approach based on Average Information Param-eters for Investigation and Diagnosis of Lung Cancer using ANN, Pattern Recognition and Image Analysis April 2018, volume 28, Issue 2, pp 301-309.
Zagrouba, E., Ouni, S., Barhoumi, W., A Reliable Image Retrieval System Based on Spatial Disposition Graph Matching, (2018) International Journal on Media Technology, 2 (1).
Ahmed KHARRAT, Mohamed Ben MESSAOUD, Nacéra BENAMRANE, and Mohamed ABID, Detection of Brain Tumor in Medical Images, International Conference on Signals, Circuits and Systems, PP 1-6, 2009.
Rajeev Ratan, Sanjay Sharma, and S. K. Sharma, Brain Tumor Detection based on Multiparameter MRI Image Analysis, ICGST-GVIP Journal, Volume (9), Issue (III), PP- 9- 17, June 2009.
Qurat-Ul-Ain, Ghazanfar Latif, Sidra Batool Kazmi, M. Arfan Jaffar, and Anwar M. Mirza, Classification and Segmentation of Brain Tumor using Texture Analysis, Recent Advances In Artificial Intelligence, Knowledge Engineering and Data Bases, PP 147- 155, Jan 2010.
M. Usman Akram and Anam Usman, Computer Aided System for Brain Tumor Detection and Segmentation, International Conference on Computer Networks and Information Technology, PP 299-302, July 2011.
Stefan Bauer, Christian May, Dimitra Dionysiou, Georgios Stamatakos, Philippe B¨uchler, and Mauricio Reyes, Multiscale Modeling for Image Analysis of Brain Tumor Studies, IEEE Transactions On Biomedical Engineering, Vol. 59, No. 1, pp. 25-29, January 2012.
R. Vijayarajan and S. Muttan, Fuzzy C-Means Clustering Based Principal Component Averaging Fusion, International Journal of Fuzzy Systems, Vol. 16, No. 2, June 2014 PP-153-159.
M. Sornam, Muthu Subash Kavitha, R. Shalini Segmentation and Classification of Brain Tumor using Wavelet and Zernike based features on MRI, IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, 166-169, Oct 2016.
Gupta K.K., Dhanda N., Kumar U. (2020) Depth Analysis of Different Medical Image Segmentation Techniques for Brain Tumor Detection. In: Jain L., Virvou M., Piuri V., Balas V. (eds) Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals. Advances in Intelligent Systems and Computing, vol 1064. Springer, Singapore.
K. K. Gupta, N. Dhanda, and U. Kumar, A Comparative Study of Medical Image Segmenta-tion Techniques for Brain Tumor Detection, 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, 2018, pp. 1-4.
Rasel Ahmmed, Anirban Sen Swakshar, Md. Foisal Hossain, and Md. Abdur Rafiq,Classification of Tumors and It Stages in Brain MRI Using Support Vector Machine and Artificial Neural Network, In Proc. IEEE International Conference on Electrical, Computer and Communication Engineering (ECCE), Bangladesh, 229-237, Feb 2017.
Mohsen Zand, Shyamala Doraisamy, Alfian Ab-dul Halin, Mas Rina Mustaffa, Texture classifica-tion and discrimination for region-based image retrieval, Journal of Visual Communication and Image Representation, Volume 26, 2015, Pages 305-316, ISSN 1047-3203.
Gorshkova, K., Zueva, V., Kuznetsova, M., Tugashova, L., Optimizing Deep Learning Methods in Neural Network Architectures, (2021) International Review of Automatic Control (IREACO), 14 (2), pp. 93-101.
Brain MRI Images for Brain Tumor Detection Dataset, 2019.
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