Detection Of Brain Tumors From Magnetic Resonance Imaging By Combining Superpixel Methods And Relevance Vector Machines Classification

Subject Areas : Majlesi Journal of Telecommunication Devices

Ebrahim  Akbari ¹ (Islamic Azad University, Mobarakeh Branch)
Mehran  Emadi ² (Islamic Azad University, Mobarakeh Branch)

Received: 2019-01-01 Accepted : 2019-01-01 Published : 2019-03-01

Keywords: Magnetic resonance imaging, Relevance vector machines classification, Super pixel classification,

Abstract :

The production of additional cells often forms a mass of tissue that is referred to as a tumor. Tumors can disrupt the proper functioning of the brain and even lead to the patients' death. One of the non-invasive diagnostic methods for this disease is Magnetic Resonance Imaging (MRI). The development of an automated or semi-automatic diagnostic system is required by the computer in medical treatments. Several algorithms have been used to detect a tumor, each with its own advantages and disadvantages. In the present study, an automatic method has been developed by the combination of new methods in order to find the exact area of the tumor in the MRI image. This algorithm is based on super pixel and RVM classification. The algorithm used in the super pixel method is the SLIC algorithm, which calculates for each super pixel 13 statistical characteristics and severity. Finally, an educational method introduced from the RVM classification algorithm that can detect the tumor portion from non-tumor in each brain MRI image. BRATS2012 dataset and FLAIR weights have been utilized in this study The results are compared with the results of the BRATS2012 data and The overlap coefficients of Dice, BF score, and Jaccard were 0.898, 0.697 and 0.754, respectively.

References:

[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.