Threshold Segmentation and Watershed Segmentation Algorithm for Brain Tumor Detection using Support Vector Machine

##plugins.themes.bootstrap3.article.main##

  •   Santhosh Kumar Hatcholli Seere

  •   K. Karibasappa

Abstract

Brain Tumor is a dangerous disease. The chance of the death is more in case of the brain tumor. The method of detection and classification of brain tumor is by human intervention with use of medical resonant brain images. MR Images may contain noise or blur caused by MRI operator performance which can lead to difficult in classification. We can apply effective segmentation techniques to partition the image and apply the classification technique. Support Vector machine is the best classification tool we identified as part of this work.  The use Support Vector Machine show great potential in this field. SVM is a binary Classifier based on supervised learning which gives better result than other classifiers. SVM classifies between two classes by constructing hyper plane in high-dimensional feature space which can be used for classification.


Keywords: Segmentation, MRI Image, SVM, Brain Tumor

References

Dr. R. J. Ramteke, KhachaneMonali Y, “Automatic Medical Image Classification and Abnormality Detection Using KNearest Neighbour”, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970), Volume-2 Number-4 Issue-6 December-2012.

Roshan G. Selkar, Prof. M. N. Thakare, Prof. B. J. Chilke, “Segmentation and Detection of Brain Tumor using watershed and thresholding Algorithm” IJRAE in April 2014.

Vijay S.Gaikwad1, Prof.S.M.Rokade,”Survey on Brain Tumor Detection Techniques Using Magnetic Resonance Images, Volume3 Issue10, Oct2014 in IJSR.

D. Anithadevi, K. Perumal, (2014) “Brain tumor extraction based on segmentation”, International journal on Recent and Innovation Trends in Computing and Communications, Vol.2, No. 9, pp: 2682-2689.

Shweta Jain, “Brain Cancer Classification Using GLCM Based Feature Extraction in Artificial Neural Network”, International Journal of Computer Science & Engineering Technology (IJCSET), ISSN: 2229-3345, Vol. 4 No. 07 Jul 2013.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
Hatcholli Seere, S. and Karibasappa, K. 2020. Threshold Segmentation and Watershed Segmentation Algorithm for Brain Tumor Detection using Support Vector Machine. European Journal of Engineering and Technology Research. 5, 4 (Apr. 2020), 516-519. DOI:https://doi.org/10.24018/ejers.2020.5.4.1902.