An Improved K-means Algorithm for Brain MRI Image Segmentation
Jianwei Liu, Lei Guo
Available Online April 2015.
- https://doi.org/10.2991/icmra-15.2015.210How to use a DOI?
- Magnetic Resonance Imaging (MRI) ;Segmentation; K-means
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icmra-15.2015.210 ID - Liu2015/04 ER -