Automatic Brain Tissue Segmentation using Modified K-Means Algorithm Based on Image Processing Techniques

Automatic Brain Tissue Segmentation using Modified K-Means Algorithm Based on Image Processing Techniques Assistant Professor, Department of Computer Science & Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India. Archana K.S Sobana J Assistant Professor, Department of Information Technology, Rajalakshmi Engineering College ,Anna University, Chennai, Tamil Nadu 602105 India Ebenezer Abishek.B Assistant Professor, Department of Master of Computer Application, SRM Institute of Science and Technology, Chennai, India. Kathiravan M Associate Professor, Department of Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India. Gopalakrishnan. S Research Scholar Department of Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.

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.
10 30 2019 664 666 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.L2660.1081219 https://www.ijitee.org/portfolio-item/L26601081219/ https://www.ijitee.org/wp-content/uploads/papers/v8i12/L26601081219.pdf