INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.8, No.11, Nov. 2016

An Automatic Segmentation of Brain Tumor from MRI Scans through Wavelet Transformations

Full Text (PDF, 490KB), PP.59-65


Views:60   Downloads:0

Author(s)

Kalaiselvi T, Nagaraja P

Index Terms

Clustering;K-means;Segmentation;Tumor;Wavelet

Abstract

Fully automatic brain tumor detection is one of the critical tasks in medical image processing. The proposed study discusses the tumor segmentation process by means of wavelet transformation and clustering technique. Initially, MRI brain images are preprocessed by various wavelet transformations to sharpen the images and enhance the tumor region. This helps to quicken the clustering technique since tumor region appears good in sharpened CSF region. Finally, a wavelet decomposition method is applied in CSF region and extracts the tumor portion. This proposed method analyzes the performance of various wavelet types such as Haar, Daubechies (db1, db2, db3, db4 and db5), Coiflet, Morlet and Symlet in MRI scans. Experiments with the proposed method were done on 5 volume datasets collected from the popular brain tumor pools are BRATS2012 and whole brain atlas. The quantitative measures of results were compared using the metrics false alarm (FA) and missed alarm (MA). The results demonstrate that the proposed method obtaining better performance in the terms of both quantity and visual appearance. 

Cite This Paper

Kalaiselvi T, Nagaraja P,"An Automatic Segmentation of Brain Tumor from MRI Scans through Wavelet Transformations", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.11, pp.59-65, 2016.DOI: 10.5815/ijigsp.2016.11.08

Reference

[1]T. Kalaiselvi and P. Nagaraja, "A Rapid Automatic Brain Tumor Detection Method for MRI Images using Modified Minimum Error Thresholding Technique" International Journal of Imaging Systems and Technology, vol.25, no.1, pp77–85, March 2015.

[2]T. Kalaiselvi, Brain Portion Extraction and Brain Abnormality Detection from Magnetic Resonance Imaging of Human Head Scans, Pallavi Publications, 2011.

[3]K. Somasundaram and T.Kalaiselvi," Automatic Detection of Brain Tumor from MRI Scans Using Maxima Transform", National Conference on Image Processing- 2010, pp.136-141, February 2010.

[4]K. Somasundaram and T.Kalaiselvi ," Fully Automatic method to Identify Abnormal MRI Head Scans using Fuzzy Segmentation and Fuzzy Symmetric Measure ", International Journal on Graphics, Vision and Image Processing (GVIP), vol.10, no.3, pp1-9, August 2010

[5]A. H. Ali, K. A. Khalaph and I. S. Nema, " Detection of Brain Tumor for MRI using Hybrid Method Wavelet and Clustering Algorithm", International Journal of Applied Information Systems, vol.6, no.7, pp.9-14, January 2014. 

[6]D. K. Kole and A. Halder, Automatic Brain Tumor Detection and Isolation of Tumor Cells from MRI Images, International Journal of Computer Applications, vol.39, no.16, pp.26-30, February 2012.

[7]P. John, "Brain Tumor Classification Using Wavelet and Texture Based Neural Network", International Journal of Scientific & Engineering Research, vol.3, no.10, pp.1-7, October-2012

[8]M. Ahmad, M. Hassan, I. Shafi and A. Osman, Classification of Tumors in Human Brain MRI using Wavelet and Support Vector Machine, IOSR Journal of Computer Engineering, vol.8, Issue 2, 2012, pp. 25-31.

[9]S.A. El-Dahshan, H.M. Mohsen, K. Revett and A.B.M. Salem. "Computer aided diagnosis of human brain tumor through MRI: A survey and a new algorithm". Expert Systems Applications, vol.41, pp.5526–5545, 2014, 

[10]P. Porwik, A. Lisowska," The Haar–Wavelet Transform in Digital Image Processing Its Status and Achievements", Machine Graphics & Vision vol.13, no.1/2, pp.79-98, 2004.

[11]A. Gavlasov, A. Prochazka and M. Mudrova," Wavelet Based Image Segmentation", 14th Annual Conference Technical computing, Prague, vol.13, 2006.

[12]A. Mustaqeem A. Javed and T. Fatima," An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation", International Journal of Image, Graphics and Signal Processing,, Vol.10, pp.34-39, 2012. 

[13]BRATS-MICCAI 2012. MRI brain image database.

[14]The Whole Brain Atlas (WBA), Department of Radiology and Neurology at Brigham and womens hospital, Harvard Medical School, Boston, USA.

[15]A. Graps, "An Introduction to Wavelets," IEEE Computational Science and Engineering, vol.2, no.2, 1995.

[16]A. K. Verma, C. Patvardhan C. Vasantha Lakshmi," Robust Adaptive Watermarking Based on Image Contents Using Wavelet Technique", International Journal of Image, Graphics and Signal Processing, vol.2, pp. 48-55, 2015,

[17]E. A. El-Dahshan, T, Hosney and A. B. M. Salem, Hybrid intelligence techniques for MRI Brain images classification, ELSEVIER, Digital Signal Processing, vol. 20, pp.433-441, 2010.

[18]K. Somasundaram and T. Kalaiselvi," A Comparative Study of Segmentation Techniques Used for MR Brain Images," The International Conference on Image Processing, Computer Vision and Pattern Recognition –IPCV'09, WORLDCOMP'09, Los Vegas, Nevada, USA, vol. I2, pp. 597-603, 2009.