Brain Tumor Segmentation using Normalized Graph Cuts
Nancy W1, A Celinekavida2
1Nancy W, Assistant Professor, Jeppiaar Institute of Technology, Chennai, India.
2A Celinekavida, Associate Professor, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3159-3161 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9258088619/2019©BEIESP | DOI: 10.35940/ijeat.F9258.088619
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Normalized graph cut algorithm is an efficient method where the technique of graph theory is adopted and in which the images are taken in the form of weighted graph in order to segment the images. This paper comprises of the fundamental concept of Normalized graph cut algorithm and its application towards the segmentation of Brain tumor. Identifying defects such as tumors is a very challenging because differentiating the components is difficult in a complex structure like a human brain. The diagnosis becomes even more complex because the tumor, blood clots and some part of the brain tissues appear as the same Brain tumor is generally detected and analyzed through a comprehensive analysis of the Magnetic Resonance Images of the brain. This technique gives a second opinion regarding the presence or absence of the brain tumor. This paper performs the study of Normalized graph cut algorithm and shows its efficiency in detecting tumors and compares it with other commonly used algorithms.
Keywords: Brain Tumor, Image Processing, Segmentation and 3D.