Automatic Brain Tissue Segmentation using Modified K-Means Algorithm Based on Image Processing Techniques
Archana K.S1, Kathiravan M2, Shobana J3, Gopalakrishnan S4, Ebenezer Abishek B5
1Archana K.S*, Assistant Professor, Department of Computer Science & Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
2Kathiravan M, Assistant Professor, Department of Information Technology, Rajalakshmi Engineering College, Anna University, Chennai, Tamil Nadu, India.
3Shobana J, Assistant Professor, Department of Master of Computer Application, SRM Institute of Science and Technology, Chennai, India.
4Gopalakrishnan.S , Research Scholar Department of Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
5Ebenezer Abishek.B, Associate Professor, Department of Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 664-666 | Volume-8 Issue-12, October 2019. | Retrieval Number: L26601081219/2019©BEIESP | DOI: 10.35940/ijitee.L2660.1081219
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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: Brain tumor, due to uncontrolled development of abnormal cells, is one of the hazardous illnesses that happen in the brain. A fully automatic brain tissue segmentation using improved k means segmentation is discussed in this paper. Generally the brain tumor tissue can appear at any location at different size and shapes. Manual brain tumor detection is not only time-consuming, it is also linked to human errors and depends on the expertise and experience of a medical pathologist. Automatic detection is required in a computer-aided detection system (CAD) for medical images such as MRI. This automatic detection includes pre-processing, segmentation and medical image classification. The preprocessing techniques eliminate noise. Separate the region of interest from the background picture using the segmentation methods. Finally, the classification is conducted to identify brain tumor automatically. The outcomes are also compared between the suggested method and the current methods.
Keywords: Image Processing, MRI, Brain Tumor, Preprocessing and Segmentation.
Scope of the Article: Algorithm Engineering