Open Access Open Access  Restricted Access Subscription or Fee Access

Brain Tumor Segmentation with Wavelet Watershed and Detection Using Multi-SVM Classifier

(*) Corresponding author

Authors' affiliations



Detection of tumour in brain has most prominence in the recent years. Various processes are proposed for detecting BRAIN TUMOUR which comprises with image segmentation and classification process. But classification process has dominant and suppressed most of the techniques by its advantages of detecting and classifying brain tumour. In this paper a novel approach of Wavelet watershed technique is proposed with MULTI RBF SVM classifier process for segmentation and classification processes respectively. The feature extraction and region segmentation processes were completed by Wavelet Watershed technique for this we used to calculate the energy of the image for a texture level classification mode. Under Multi SVM classifier the weight comes into play for training datasets along with classification mode. Experimental results are acquired from the proposed technique is about 95%.
Copyright © 2014 Praise Worthy Prize - All rights reserved.


Brain Tumour; Wavelet; Watershed; Segmentation; Classifier; RBF (Radial Basis Function); SVM (Support Vector Machine)

Full Text:



Lamia Jaafar Belaid and Walid Mourou, “Image Segmentation: A Watershed Transformation Algorithm” Image Anal Stereol 2009; 28:93=102.

Rittner. L;Ssh of Electr & Comput Eng, University of Campinas UNICAMP, Campinas, Brazil; Appenzeller. S; Lotufo. R, “Segmentation of Brain Structures by watershed Transform on Tensorial Morophological Gradient of Diffussion Tensor Imaging”, IEEE TRANSACTION 11-15 oct, 2009.

Chen- Jia- Xin, Liu sen, “A Medical image segmentation using watershed Transform”, IEEE Trans, 21-23 Sept, 2005.

Ning Li, Miaomiao, Youfu Li, “ Image Segmentation Algorithm using Watershed Transform and Level set Method”, IEEE Conference 2007.

Wayne state university, RADIOLOGIC ANATOMY, http:// www. med. wayne. edu/ diagradiology/ anatomy_modules/ brain/ brain.html.

Abdulla, Nibong Tebla Nagh, “Image classification of brain mri using support vector machine”, IEEE International Conference on 17-18 May 2011.

Mark Schmidt, Albert Murtha, “Segmenting brain tumours using alignment”, IEEE International Conference on Machine Learning and Applications 2005.

Varsh K Shirsagar, Jagruthi Panchal, “Segmentation of Brain Tumour and Its Area Calculation”, International Journal of Advanced Research in Computer Science and Software Engineering, MAY, 2014.

Selvakumar, Lakshmi, Arivoli, “Brain Tumour Segmentation and Its ae calculation in brain MR images using K-Mean Clustering and Fuzzy C-mean algorithm”, IEEE conference, March 2012.

Pham, Johns Hopkins, “Unsupervised tissue classification in medical images using edge-adaptive clustering”, IEEE transactions 634-637 Vol1. 17-21 Sept. 2003.

Kavitha, Chellamuthu “An efficient approach for brain tumour detection based on modified region growing and neural network in MRI images.” 1087-1095 Vol1. 21-22 March 2012.

Hopkins, “http://www. hopkinsmedicine. org/ neurology_neurosurgery/ centers_clinics/ brain_tumor/ diagnosis/ how-to-diagnose-brain-tumors. html”.

NHS choices , http://www.nhs. Uk / Conditions /brain – tumours / Pages / Introduction. aspx”.

A. D. Jepson and D. J. Fleet, “Image segmentation”, 2007.

Jean-Lucstaack, Fionn Murtagh, Jalal M. Fadili, “Sparse image and signal processing”, Cambridge University.

Sravani, Ramesh reddy, “an enhanced glaucoma detection using fdct using multi svm”, IJERT, August 2014.

http:// nlp. stanford. edu/ IR-book/ html/ html edition/ support- vector- machines- the- linearly- separable- case- 1. Html.

Hajer, J., Kamel, H., Interactive three-dimensional segmentation of MR images by hierarchical watershed, (2009) International Review on Computers and Software (IRECOS), 4 (2), pp. 183-187.

Khazaee, A., Ebrahimzadeh, A., Electrocardiogram beat classification using support vector machines and efficient features, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (6), pp. 515-521.

Zhi-Hang, T., Bei-Ping, T., Han, Y., Yi-Jie, L., Xiang-Ling, L., Wen-Bin, T., Hai-Bin, W., A new pattern recognition method based on nonlinear support vector machine, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 262-266.

Chitra, S., Kalpana, B., Identifying interesting visitors through transductive support vector machine web log classifier, (2014) International Review on Computers and Software (IRECOS), 9 (2), pp. 390-395.

Ni, Z., Gu, L., Wu, Z., Zhang, C., Support vector machine based knowledge discovery in CBR system, (2012) International Review on Computers and Software (IRECOS), 7 (1), pp. 369-373.

Li, Y., Ma, P., Yu, L., LS-SVM soft sensing based on hybrid particle swarm optimization, (2012) International Review on Computers and Software (IRECOS), 7 (1), pp. 283-289.

Marrakchi, O., Agina, A., Elghali, A., Evaluating SVM and BPNN classifiers for remote sensing data, (2009) International Review on Computers and Software (IRECOS), 4 (5), pp. 600-605.

Abdelhamid, D., Chaouki, B.M., Abdelmalik, T.A., An SVM based system for automatic dates sorting, (2010) International Review on Computers and Software (IRECOS), 5 (4), pp. 423-428.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize