A Novel Approach for Segmentation of Brain Image using a Multiscale Transform and a Region Based Active Contour

Authors

  • Smitha P Ullal Research Scholar, Department of Electronics and Communication,BMS College of Engineering,Basavanagudi,Bangalore, Karnataka India
  • Dr.Nanjraj C P Professor & Head, Dept of Radiodiagnosis, MMC & RI,Mysore
  • Dr. Meera A Professor, Dept of Electronics and Communication, BMSCE, Basavanagudi,Bengaluru-19

DOI:

https://doi.org/10.14738/jbemi.56.5748

Keywords:

Multiscale, Multiresolution Transform, Chanvese active contour, Curvelet transform

Abstract

Over the Past decade Medical Image segmentation is one of the most challenging and focused topic for intensive research in interdisciplinary areas of Image processing and computer vision. Segmentation is the process of automatic or semi-automatic detection of boundaries [5]. In this paper, we implement a novel unsupervised method for segmenting MRI brain Images based on multiresolution transforms and region based active contour. Application of multiscale, multiresolution methods with active contour is most interesting research topic in image segmentation [6].This new application makes segmentation algorithms more economical for computation.

References

(1) Liu, Zhigui, Junbo Wang, and Yuyu Zhu. "A study of active contour segmentation models based on automatic initial contour." International Journal of Signal Processing, Image Processing and Pattern Recognition 8.4 (2015): 201-214.

(2) Wu, H., Liu, J., Chui, C., 2000. A wavelet-frame based image force model for Active contouring algorithms. IEEE Trans. Image Process. 9 (11), 1983–1988.

(3) Mignotte, M., Meunier, J., 2001. A multiscale optimization approach for the dynamic contour-based boundary detection issue. Comput. Med. Imaging Graph. 25 (3), 265–275.

(4) Singh R& Khare, A. (2013). Multiscale medical image fusion in wavelet domain. The Scientific World Journal, 2013

(5) Ali, Rehan, et al. "Semi-automatic segmentation of subcutaneous tumors from micro-computed tomography images." Physics in medicine and biology 58.22 (2013): 8007.

(6) Shan, Hao, and Jianwei Ma. "Curvelet-based geodesic snakes for image segmentation with multiple objects." Pattern Recognition Letters 31.5 (2010): 355-360.

(7) Wang, Guodong, et al. "Unsupervised texture segmentation using active contour model and oscillating information." Journal of Applied Mathematics 2014 (2014).

(8) Li, Ling, et al. "Multiscale Geometric Active Contour Model and Boundary Extraction in Kidney MR Images." International Conference on Health Information Science. Springer, Cham, 2014.

(9) Bresson, Xavier, Pierre Vandergheynst, and Jean-Philippe Thiran. "Multiscale active contours." International Journal of Computer Vision 70.3 (2006): 197-211.

(10) Alvino, C. V. (2005). Multiscale active contour methods in computer vision with applications in tomography (Doctoral dissertation, Georgia Institute of Technology).

(11) Al-Qunaieer, Fares S., Hamid R. Tizhoosh, and Shahryar Rahnamayan. "Multi-resolution level set image segmentation using wavelets." Image Processing (ICIP), 2011 18th IEEE International Conference on. IEEE, 2011.

(12) Kass, Michael, Andrew Witkin, and Demetri Terzopoulos. "Snakes: Active contour models." International journal of computer vision 1.4 (1988): 321-331.

(13) Chen, Da, Dengwang Li, and Mingqiang Yang. "Active contour for noisy image segmentation based on contourlet transform." Journal of Electronic Imaging 21.1 (2012): 013009-1.

(14) Law, Yan Nei, Hwee Kuan Lee, and Andy M. Yip. "A multiresolution stochastic level set method for Mumford–Shah image segmentation." IEEE transactions on image processing 17.12 (2008): 2289-2300.

(15) Kalavathi, P., and T. Priya. "Segmentation of Brain Tissue in MR Brain Image using Wavelet Based Image Fusion with Clustering Technique."

(16) J. L. Starch , E. J Candes , D. L. Donoho ,“The Curvelet Transform for Image Denoising” , IEEE Trans on Image Processing, vol . 11 , Issue 6 , pp 670 – 684 , 2002

(17) Starck, Jean-Luc, Emmanuel J. Candès, and David L. Donoho. "The Curvelet transform for image denoising." IEEE Transactions on image processing 11.6 (2002): 670-684.

(18) Singh, Rajat, and D. S. Meena. "Image Denoising Using Curvelet Transform." Department of Computer Science and Engineering National Institute of Technology, Rourkela.

(19) Aili, Wang, et al. "Image denoising method based on curvelet transform." Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on. IEEE, 2008.

(20) Ma, Jianwei, and Gerlind Plonka. "Computing with curvelets: from image processing to turbulent flows." Computing in Science & Engineering 11.2 (2009): 72-80.

(21) AlZubi, Shadi, Naveed Islam, and Maysam Abbod. "Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation." Journal of Biomedical Imaging 2011 (2011): 4.

(22) Dettori, Lucia, and Lindsay Semler. "A comparison of wavelet, Ridgelet, and Curvelet-based Texture classification algorithms in computed tomography." Computers in biology and medicine 37.4 (2007):

-498.

(23) Candès, Emmanuel J., and Franck Guo. "New Multiscale Transforms, minimum total variation synthesis: Applications to edge-preserving image reconstruction." Signal Processing 82.11 (2002): 1519-1543.

(24) Emmanuel Candes, Laurent Demanet, David Donoho and Lexing Ying, “Fast Discrete Curvelet Transforms”, ACM, March 2006

(25) Chan, Tony F., B. Yezrielev Sandberg, and Luminita A. Vese. "Active

contours without edges for vector-valued images." Journal of Visual Communication and Image Representation 11.2 (2000): 130-141.

(26) Chan, Tony F., and Luminita A. Vese. "Active contours without edges." IEEE Transactions on image processing 10.2 (2001): 266-277.

(27) Mumford, D. and J. Shah, 1989. Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Applied Math., 42: 577-685.

(28) Williams, Donna J., and Mubarak Shah. "A fast algorithm for active contours and curvature estimation." CVGIP: Image understanding 55.1 (1992): 14-26.

(29) Osher, Stanley, and Ronald P. Fedkiw. "Level set methods: an overview and some recent results." Journal of Computational physics 169.2 (2001): 463-502.

(30) Gobbino, Massimo. "Finite difference approximation of the Mumford‐Shah functional." Communications on pure and applied mathematics 51.2 (1998): 197-228.

(31) Vese, Luminita A., and Tony F. Chan. "A multiphase level set framework for image segmentation using the Mumford and Shah model." International journal of computer vision 50.3 (2002): 271-293.

(32) Bresson, Xavier, Pierre Vandergheynst, and Jean-Philippe Thiran. "Multiscale active contours." International Journal of Computer Vision 70.3 (2006): 197-211.

Downloads

Published

2019-01-01

How to Cite

Ullal, S. P., C P, D., & Meera A, D. (2019). A Novel Approach for Segmentation of Brain Image using a Multiscale Transform and a Region Based Active Contour. Journal of Biomedical Engineering and Medical Imaging, 5(6), 18. https://doi.org/10.14738/jbemi.56.5748