An Efficient Method K Means Clustering for Detection of Tumour Volume in Brain MRI Scans

Here in this paper we discuss about an efficient method k means clustering for detection of tumour volume in brain MRI scans. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumour tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and K means clustering techniques for grouping tissues belonging to a specific group. The developments in the application of information technology have completely changed the world. The obvious reason for the introduction of computer system is reliability, accuracy, simplicity and ease of use. Besides, the customization and optimization features of a computer system and among the other major driving forces in adopting and subsequently strengthening the computer aided systems. On medical imaging, an image is captured, digitized and processed fordoing segmentation and for extracting important information. Manual segmentation is an alternate method for segmenting an image. This method is not only tedious and time consuming, but also produces inaccurate results. Therefore, there is a strong need to have some efficient computer based system that accurately defines the boundaries of brain tissues along with minimizing the chances of user interaction with the system. Ananthagiri Vijaya Saradhi | L. Srinivas "An Efficient Method K-Means Clustering for Detection of Tumour Volume in Brain MRI Scans" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd13027.pd