Affiliations: [a]
Research Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| [b]
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| [c]
Department of Computer Science, Faculty of computers and information, Mansoura University, Mansoura City, Egypt
| [d]
Misr Higher Institute for Engineering and Technology, Mansoura City, Egypt
| [e]
School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, China
| [f] Key Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
Correspondence:
[*]
Corresponding author: Qingmao Hu, Research Laboratory for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Shenzhen, China. Tel.: +86 0755 86392214; Fax: +86 075586392299; E-mail: qm.hu@siat.ac.cn.
Abstract: Brain tissue segmentation from magnetic resonance (MR) images is an importance task for clinical use. The segmentation process becomes more challenging in the presence of noise, grayscale inhomogeneity, and other image artifacts. In this paper, we propose a robust kernelized local information fuzzy C-means clustering algorithm (RKLIFCM). It incorporates local information into the segmentation process (both grayscale and spatial) for more homogeneous segmentation. In addition, the Gaussian radial basis kernel function is adopted as a distance metric to replace the standard Euclidean distance. The main advantages of the new algorithm are: efficient utilization of local grayscale and spatial information, robustness to noise, ability to preserve image details, free from any parameter initialization, and with high speed as it runs on image histogram. We compared the proposed algorithm with 7 soft clustering algorithms that run on both image histogram and image pixels to segment brain MR images. Experimental results demonstrate that the proposed RKLIFCM algorithm is able to overcome the influence of noise and achieve higher segmentation accuracy with low computational complexity.
Keywords: Brain tissue segmentation, magnetic resonance images, fuzzy C-means, grayscale and spatial information, Gaussian radial basis kernel, image histogram
