An Improved K-means Algorithm for Brain MRI Image Segmentation

DOI

10.2991/icmra-15.2015.210How to use a DOI?

Keywords

Magnetic Resonance Imaging (MRI) ;Segmentation; K-means

Abstract

For the problem of low accuracy by the traditional K-means clustering algorithm to segment noised brain magnetic resonance imaging (MRI) images. This paper proposed an improved K-means algorithm. The traditional K-means algorithm only considers the brain image gray value itself, ignoring the relationship between pixels. Due to the characteristics of brain MRI image adjacent pixels most likely belonging to the same class, this paper adopts average value of small neighborhood of each image pixel and image gray value to compose a new sample point, in order to reduce the impact of noise on the clustering accuracy. Experimental results show that the improved K-means algorithm can effectively improve the segmentation accuracy of the noised brain MRI image.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Jianwei Liu
AU  - Lei Guo
PY  - 2015/04
DA  - 2015/04
TI  - An Improved K-means Algorithm for Brain MRI Image Segmentation
BT  - Proceedings of the 3rd International Conference on Mechatronics, Robotics and Automation
PB  - Atlantis Press
SP  - 1087
EP  - 1090
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmra-15.2015.210
DO  - 10.2991/icmra-15.2015.210
ID  - Liu2015/04
ER  -