Detection Of Brain Tumors From Magnetic Resonance Imaging By Combining Superpixel Methods And Relevance Vector Machines Classification
Subject Areas : Majlesi Journal of Telecommunication DevicesEbrahim Akbari 1 , Mehran Emadi 2
1 - Islamic Azad University, Mobarakeh Branch
2 - Islamic Azad University, Mobarakeh Branch
Keywords:
Abstract :
[1] V. L. and S. P., "Watersheds in digital spaces: an efficient algorithm based on immersion simulations," IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 583-598, 1991.
[2] A. kharrat, "Detection of brain tumor in medical images," in 2009 3rd International Conference on Signals, Circuits and Systems (SCS), 2009.
[3] S. K., S. V. and T. S., "Clustering based Segmentation Approach to Detect Brain Tumor from MRI Scan," International Journal of Computer Applications, p. 118, 2015.
[4] S., Shen, "MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization," IEEE transactions on information technology in biomedicine, pp. 459-467, 2005.9(3).
[5] J. Selvakumar and A. Lakshmi, "Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm," in IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM-2012 ), 2012.
[6] H. M.A., "Lung Cancer Detection Using Artificial Neural Network & Fuzzy Clustering," International Journal of Advanced Research in Computer and Communication Engineering, 2015.4.(3).
[7] V. Rajesh, "Brain Tumor Segmentation and its Area Calculation in Brain MR Images using K-Mean Clustering and Fuzzy C-Mean Algorithm.," Fuzzy Systems, pp. 103-107, 2015.
[8] Y. Sharma and p. Kaur, "Detection and extraction of brain tumor from MRI images using k-Means clustering and watershed algorithms," International Journal of Computer Science Trends and Technology, pp. 8-32, 2015. 3(5).
[9] P. Sangamithraa and S. Govindaraju, "Lung tumour detection and classification using EK-Mean clustering," in International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). 2016., 2016.
[10] G. Praveen and A. Agrawal, "Hybrid approach for brain tumor detection and classification in magnetic resonance images," in 2015 Communication, Control and Intelligent Systems (CCIS), 2015.
[11] S. Ji, "A new multistage medical segmentation method based on superpixel and fuzzy clustering," in Computational and mathematical methods in medicine 2014, 2014.
[12] BRATS, "The virtual skeleton database project," [Online]. Available: https://www.smir.ch/BRATS/Start2012. [Accessed 2 1 2018].
[13] M. Soltaninejad and G. Yang, "Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI," International Journal of Computer Assisted Radiology and Surgery, vol. 12, no. 2, pp. 183-203.
[14] C. DL Pham and. P. J.L. , "A Survey of Current Methods in Medical Image Segmentation," Annual Review of Biomedical Engineering, vol. 2, 2000.
[15] M. e. a. B. H., "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)," IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, 2015.
[16] A. Islam, S. M. S. Reza and K. M. Iftekharu, "Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors," IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 60, no. 11, 2013.