Modified Tumour Cut Algorithms For MRI Image Segmentation of Brain Tumours
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - June Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-6 | ||
| Year of Publication : 2013 | ||
| Authors :C.Kavitha, Prathiba.S, Antony Judice.A |
C.Kavitha, Prathiba.S, Antony Judice.A "Modified Tumour Cut Algorithms For MRI Image Segmentation of Brain Tumours"International Journal of Computer Trends and Technology (IJCTT),V4(6):1908-1912 June Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - The brain tumour segmentation methods rely on the intensity enhancement. Among them, a clustering method have been investigated and used. In this paper, CA (Cellular Automata) based seeded tumour segmentation algorithm is proposed. Which determine the Volume of Interest (VOI) and seed selection. First, establish the connection of the CA-based segmentation to the graph-cut method to show that the iterative CA framework solves the shortest path problem. This paper describe segmentation method consist of two phases. In the first phase, the MR Image is acquired from patient database and contrast enhancing the image. In the second phase, the CA algorithm run twice for background seed (healthy cell) and foreground seed (tumour cell) for probability calculation. Furthermore, apply Graph- Cut (GC) method to differentiate necrotic and enhancing tumour tissue content, which gains importance for a detailed assessment of radiation therapy response.
References-
[1] Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images,” in Proc. ICCV, 2001, pp. 105–112.
[2] Mohammad Shajib Khedem, “MRI image segmentation using graph-cut”, Signal Process. Group, Sweden 2010
[3] M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, “A brain tumor segmentation framework based on outlier detection,” Med. Image Anal., vol. 8, no. 3, pp. 275–283, 2004.
[4] E. D. Angelini, O. Clatz, E. Mandonnet, E. Konukoglu, L. Capelle, and H. Duffau, “Glioma dynamics and computational models: A review of segmentation, registration, and in silico growth algorithms and their clinical applications,” Curr. Med. Imag. Rev., vol. 3, no. 4, pp. 262–276, 2007.
[5] A. Sinop and L. Grady, “A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm,” in ICCV, 2007, pp. 1–8.
[6] J. Kari, “Theory of cellular automata: A survey,” Theoretical Comput. Sci., vol. 334, no. 1–3, pp. 3–33, 2005.
[7] A. Hamamci, G. Unal, N. Kucuk, and K. Engin, “Cellular automata segmentation of brain tumors on post contrast MR images,” in MICCAI. New York: Springer, 2010, pp. 137–146.
[8] Andac Hamamci*, Nadir Kucuk, Kutlay Karaman, Kayihan Engin, and Gozde Unal, “ Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications”IEEE Trans.Med.Imag.VOL.31,NO.3,MARCH 2012.
Keywords : Cellular Automata (CA), Magnetic Resonance Imaging (MRI), Necrotic region, Radiotherapy, Seeded segmentation.