Abstract
MRI is one of the tool for detecting the tumor in any part of the body. But precise tumor segmentation from such Magnetic resonance imaging (MRI) is difficult and also time consuming technique. To overcome such difficulty, this work proposes a very simple, efficient and automatic segmentation and classification of brain tumor. The proposed system is composed of four stages to segment, detect and classified tumor as benign and malignant. Pre-processing is carried out in the first stage after which watershed segmentation technique is applied for segmenting the image which is the second stage. The segmented image undergo for post processing to remove the unwanted segmented image so as to obtain only the tumor image. In the last stage, gray-level co-occurrence matrix (GLCM) is used to extract the feature. This feature is given as input to Support Vector Machine (SVM) to classify the brain tumor. Results and experiment shows that the proposed method accurately segments and classified the brain tumor in MR images.
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References
Joseph, R. Paul, M. Manikandan, C. Senthil Singh, Brain tumor MRI image segmentation and detection in image processing. Int. J. Res. Eng. Technol. 03(01 | NC-WiCOMET-2014) (2014)
Cancer Facts & Figure 2015, American Cancer Society
K. Ain, R. Rulaningtyas, Edge detection for brain tumor pattern recognition. Physics Department, University of Airlangga Surabaya
W. Yang, M. Huang, W. Chen, Y. Wu, J. Jiang, Brain tumor segmentation based on local independent projection-based classification. IEEE Trans. Biomed. Eng. 61(10) (2014)
H. Tang, W. Liu, H. Lu, X. Tao, Tumor Segmentation from Single Contrast MRI Images of Human Brain (Healthcare Philips Research China, IEEE, 2015)
R. Helen, N. Kamaraj, CAD Scheme to detect brain tumour in MR images using active contour models and tree classifiers. J. Electr. Eng. Technol. 10(2), 742 (2014)
Amritpal Singh, Parveen, Detection of brain tumor in MRI images, using combination of fuzzy C-Means and SVM, in 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)
Dr. J. Jayakumari, K.S. Angel Viji, Automatic detection of brain tumor based on magnetic resonance image using cad system with watershed segmentation, in Proceedings of 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN 2011)
K. Amshakala, K.B. Vaishnavee, An automated MRI brain image segmentation and tumor detection using SOM clustering and proximal support vector machine classifier, in 2015 IEEE International Conference on Engineering and Technology (ICETECH), 20 Mar 2015, Coimbatore, TN, India
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Singh, N.P., Dixit, S., Akshaya, A.S., Khodanpur, B.I. (2017). Gradient Magnitude Based Watershed Segmentation for Brain Tumor Segmentation and Classification. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-10-3156-4_65
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DOI: https://doi.org/10.1007/978-981-10-3156-4_65
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