Modified Self-Organizing Map Algorithm for Brain Tumor Detection and Analysis Using Magnetic Resonance Brain Images
DOI:
https://doi.org/10.14738/jbemi.62.6657Keywords:
MRI, brain tumor detection, modified self-organizing map, accuracy valuesAbstract
medical image processing play an important role to help radiologists and support their decisions in diagnosis of the patient, magnetic resonance imaging (MRI) has ability to diagnosis the small details in the human body with a high resolution; in this paper, we propose modified self-organizing map algorithm (MSOM) for brain tumor detection and analysis using magnetic resonance brain images the significance of the (MSOM) algorithm is ability to detect tumor area in the magnetic resonance brain image (MRI) clearly with a high accuracy and best performance according of different values, the advantage of method proposed can segment and detect different types of MRI brain images FLAIR, T1 and T2-weight images with same performance and accuracy, the (MSOM) method start through input magnetic resonance brain image (MRI) and preprocessing applied to remove the noise from the image, applied modified self-organizing map (MSOM), applied tumor area, performance of the method, finally the applied of modified self-organizing map (MSOM) gave a best results us shown in the results.
References
Beutel, J., Kundel, H. L., & Van Metter, R. L. (2000). Handbook of medical imaging: Physics and psychophysics (Vol. 1). Spie Press.
Esquenazi, Yoshua, Victor P. Lo, and Kiwon Lee. ”Critical care management of cerebral edema in brain tumors.” Journal of intensive care medicine 32.1 (2017): 15-24.
Ortiz. J, Gorrizb J.M, Ramezb. J, Salas-Gonzalezb, D and Llamas-Elvirac J.M, Two Fully-unsupervised methods for MR brain image segmentation using SOM-based Strategies, Applied soft computing, (2013), 2668–2682.
Logeswari. T and Karnan M, An improved implementation of brain tumor detection Using segmentation based on soft computing, Journal of Cancer Research and Experiment Oncology (JCREO(2010), 2(1) , 06-14.
Logeswari. T and Karnan M, an Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self-Organizing Map, International Journal Of Computer Theory and Engineering, (2010).Vol. 2, No. 4.
Yan Li and Zheru Chi, MR Brain Image Segmentation Based on Self-Organizing Map Network, International Journal of Information Technology, (2005) Vol. 11, No. 8.
Guler, Demirhan. A and Karakis. R, Interpretation of MR images using self-organizing Maps and knowledge-based expert systems, Digital Signal Processing, 19 (2009), 668–677.
Ong. S, Yeo. N, Lee. K, Venkatesh. Y and Cao. D, Segmentation of color images using A Two-stage selforganizing network, Image and Vision Computing, (2002), 20, 279–289.
Alirezaie. J, M. Jernigan and C. Nahmias, Automatic segmentation of cerebral MR Images using artificial neural networks, IEEE Transactions on Nuclear Science, (1998), 45 (4) , 2174–2182.
Sun. W, Segmentation method of MRI using fuzzy Gaussian basis neural network, Neural Information Processing, (2005), 8 (2), 19–24.
Ipek Oguz and Milan Sonka, LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain, IEEE Transactions on Medical Imaging, (2014), Vol. 33, No. 6, 1220 –1235.
Ayse Demirhan and Inan Guler, Combining stationary wavelet transform and Self-organizing maps for brain MR image segmentation, Engineering Applications of Artificial Intelligence, (2011), 24, 358–367.
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Copyright (c) 2019 Isselmou Abd El kader
This work is licensed under a Creative Commons Attribution 4.0 International License.
