Open Access
MATEC Web of Conferences
Volume 150, 2018
Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
Article Number 06025
Number of page(s) 6
Section Information & Communication Technology (ICT), Science (SCI) & Mathematics (SM)
Published online 23 February 2018
  1. D. Singh, K. Kaur, (2012). International Journal of Engineering and Advanced Technology (IJEAT), Classification of Abnormalities in Brain MRI Images Using GLCM, PCA and SVM., vol.1, no.6, pp.243–248. [Google Scholar]
  2. R. Kumari, (2013). SVM Classification an Approach on Detecting Abnormality in Brain MRI Images. International Journal of Engineering Research and Applications (IJERA), vol.3, no.4, pp.1686–1690. [Google Scholar]
  3. S. S. Salankar, V. R. Bora, (2014). MRI Brain Cancer Classification Using Support Vector Machine. Electrical, Electronics and Computer Science (SCEECS), IEEE Students' Conference. pp. 1–6. [Google Scholar]
  4. A. Mustaqeem, A. Javed, T. Fatima, (2012). An Efficient Brain Tumor Detection Algorithm Using Watershed and Thresholding Based Segmentation. International Journal of Image, Graphics and Signal Processing, vol.4, no.10, pp.34–39. [CrossRef] [Google Scholar]
  5. K. Somasundaram, T. Kalaiselvi, (2010). Automatic Detection of Brain Tumor from MRI Scans Using Maxima Transform. UGC Sponsored National Conference on Image Processing-NCIMP. pp. 136–141. [Google Scholar]
  6. A. Amrutal, A. Gole, Y. Karunakar, (2010). A Systematic Algorithm for 3-D Reconstruction of MRI based Brain Tumors using Morphological Operators and Bicubic Interpolation. Computer Technology and Development (ICCTD), 2nd International Conference on. IEEE. pp.305–309. [Google Scholar]
  7. H. Selvaraj, S. T. Selvi, D. Selvathi, L. Gewali, (2007). Brain MRI Slices Classification Using Least Squares Support Vector Machine. International Journal of Intelligent Computing in Medical Sciences and Image Processing, vol.1, no.1, pp.21–33. [CrossRef] [Google Scholar]
  8. N. Abdullah, U. K. Ngah, S. A. Aziz, (2011). Image Classification of Brain MRI Using Support Vector Machine. Imaging Systems and Techniques (IST), IEEE International Conference, pp. 242–247. [Google Scholar]
  9. K. Somasundaram, P. Kalavathi, (2011). Medical Image Binarization Using Square Wave Representation. First Edition. Springer-Verlag Berlin Heidelberg. [Google Scholar]
  10. S. Jain, (2013). Brain Cancer Classification Using GLCM Based Feature Extraction in Artificial Neural Network. International Journal of Computer Science and Engineering Technology, vol.4, no.7, pp.966–970. [Google Scholar]
  11. S. Kotte, P. R. Kumar, S. K. Injeti, (2016). An Efficience Approach for Optimal Multilevel Thresholding Selection for Gray Scale Images Based on Improved Differential Search Algorithm. Ain Shams Engineering Journal. [Google Scholar]
  12. A. Hamamci, G. Unal, (2012). Multimodal Brain Tumor Segmentation Using The “Tumor-Cut” Method on The BraTS Dataset. Process MICCAI-BRATS (Multimodal Brain Tumor Segmentation Challenge), pp.19–23. [Google Scholar]
  13. T. Zhang, Y. Xia, D. D. Feng, (2012). Clonal Selection Algorithm for Gaussian Mixture Model Based Segmentation of 3D Brain MR Images. Intelligent Science and Intelligent Data Engineering. Pringer Berlin Heidelberg., pp.295–302. [CrossRef] [Google Scholar]
  14. J. G. Park, C. Lee, (2009). Skull Stripping Based on Region Growing for Magnetic Resonance Brain Images. NeuroImage, vol.47, no.4, pp.1394–1407. [CrossRef] [Google Scholar]
  15. K. Somasundaram, T. Kalaiselvi, (2011). Automatic Brain Extraction Methods for T1 Magnetic Resonance Images Using Region Labeling and Morphological Operations. Computers in Biology and Medicine, vol.41, no.8, pp.716–725. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.