Automated Brain Tumor Detection and Segmentation from MRI Images using Adaptive Connected Component Pixel Segmentation
J. Martin Sahayaraj1, N.Subash2, S.Jaya Pratha3, N. Tamilarasan4
1J. Martin Sahayaraj*, Department of Electronics and Communication Engineering, Sri Indu College of Engineering and Technology, Hyderabad, T.S, India
2N.Subash, Department of Electronics and Communication Engineering, Sri Indu College of Engineering and Technology, Hyderabad, T.S, India.
3S.Jaya Pratha, Research Scholar, Department of CSE, Annamalai University, Chidambaram, T.N, India.
4N. Tamilarasan, Department of Electronics and Communication Engineering, Sri Indu College of Engineering and Technology, Hyderabad, T.N, India
Manuscript received on October 14, 2019. | Revised Manuscript received on 21 October, 2019. | Manuscript published on November 10, 2019. | PP: 2642-2645 | Volume-9 Issue-1, November 2019. | Retrieval Number: I7484078919/2019©BEIESP | DOI: 10.35940/ijitee.I7484.119119
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© 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: Magnetic resource imaging (MRI) imagesare used in examining the soft tissues which include brain tumors, ligament and tendon injury, spinal cord injury. Gray scale image processing is good for basic segmentation application. The exact location of brain tumor and its length is hard to find. This paper proposes an efficient method to segment the brain tumor. The result shows good segmentation accuracy. Keywords- Connected component, Pixel Segmentation, Image morphology, Brain tumor, Brain cancer, Medical images, MRI images
Keywords: Connected Component, Pixel Segmentation, Image Morphology, Brain Tumor, Brain Cancer, Medical Images, MRI Images
Scope of the Article: Image Processing and Pattern Recognition