An Automated Method for Brain Tumor Segmentation Based on Level Set

  • Maryam Taghizadeh Dehkordi Faculty of Technology and Engineering, Shahrekord University, Shahrekord, Iran
Keywords: Tumor segmentation, Multi-scale fuzzy filter, feature, energy function, Level set.


 In this paper, an automatic method has been proposed for tumor segmentation. In this method, a new energy function by introducing the feature tumor is determined implemented by level set. Multi-scale Morphology Fuzzy filter is applied to the image and its output determines the tumor feature. The initial contour selection is important in active contour models. Therefor the initial contour has been selected automatically by using Hough transform and morphology function. Experimental results on MR images verify the desirable performance of the proposed model in comparison with other methods.


Chi. Lee, Sh. Wang, A. Murtha, Ma R. G. Brown and R. Greiner, “Segmenting Brain Tumors using Pseudo Conditional Random Fields.” Med Image Comput Comput Assist Interv., vol.11, pp. 359-66. 2008.

N. Bh Bahadure, A. K. Ray, and H. P. Thethi, “Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM.” International journal of Biomedical Imaging Vol. 2017, ID. 9749108, 2017

J. Liu , J.K. Udupa , D. Odhner , D. Hackney , G. Moonis , “A system for brain tumor volume estimation via MR imaging and fuzzy connectedness,” Comput Med Imaging Graph., vol. 29, no.1, pp. 21-34, Jan 2005.

G.Moonisa, J.Liub, J.K.Udupab and D.B.Hackneya, “Estimation of Tumor Volume with Fuzzy-Connectedness Segmentation of MR Images. ” American journal of neuroradiology, Vol.38, no.2, 2001.

L.Zhao and K.Jia. “Multiscale CNNs for Brain Tumor Segmentation and Diagnosis,” Computational and Mathematical Methods in Medicine, Vol. 2016 , Article ID 8356294, 2016

T.Weglinski and A.Fabijanska, “Brain tumor segmentation from MRI data sets using region growing approach, ” in Proceedings of the 7th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH '11), pp. 185–188, May 2011.

V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput.Vis, vol. 22, pp. 61–79, 1997.

Y. Xiang, A.C.S Chung, J. Ye, “An active contour model for image segmentation based on elastic interaction.” Journal of Computational Physics, pp. 455–476, 2006.

N. Paragios and R. Deriche, “Geodesic active regions and level set methods for supervised texture segmentation.” Int. J. Computer.Vis., vol.46, pp. 223–247, 2002.

T. Chan and L. Vese, “Active contours without edges.” IEEE Trans.Image Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.

X. Xie, M. Mirmehdi, “MAC: Magnetostatic Active Contour Model.” IEEE Tran.Pat.Ana, vol.30, no.4, april 2008.

C. Li, C.Y. Kao, J.C. Gore and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation.” IEEE Trans. Image Process., 2008, 17, (10), pp. 1940–1949

M.T. Dehkordi, A. Doosthoseini, S. Sadri, and H. Soltanianzadeh, “local feature fitting active contour for segmenting vessel in angiograms.” IET computer vision, Vol.8, no.3, pp.161-170, 2014.

K. Zhang, H. Song and L. Zhang, “Active contours driven by local image fitting energy”, Patt. Recognit., 2010, 43, pp. 1199–1206M.

M. T. Dehkordi, M. Jalalat, S. Sadri, A. Doosthoseini, M. Ahmadzadeh, and R. Amirfattahi, “Vesselness-guided Active Contour: A Coronary Vessel Extraction Method.” J Med Signals Sens., vol.4, no. 2, pp.150–157, Apr-Jun 2014.

K. Zhang, L. Zhang, K. M. Lam, and D. Zhang, “A Level Set Approach to Image Segmentation With Intensity Inhomogeneity.” IEEE transactions on cybernetcs, V0l.46. no.2, pp.546-57, 2016 Feb

T. Lindeberg, “Scale-Space Theory in Computer Vision.” Kluwer Academic, Dordrecht, The Netherlands, 1994

M. T. Dehkordi, “A new active contour model for tumor segmentation.” 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA2017), pp.233-236.

K. Sun, Zh. Chen, Sh. Jiang and Yu. Wang, “Morphological Multiscale Enhancement Fuzzy Filter and Watershed for Vascular Tree Extraction in Angiogram.” Journal of Medical Systems 35(5):811-24 • October 2011

How to Cite
Dehkordi, M. T. (2018). An Automated Method for Brain Tumor Segmentation Based on Level Set. International Journal of Computer (IJC), 30(1), 59-69. Retrieved from