Local gaussian distribution fitting based FCM algorithm for brain MR image segmentation

Authors:
Zexuan Ji;Yong Xia;Quansen Sun;Deshen Xia;David Dagan Feng
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China,Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Informatio ...;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;Biomedical and Multimedia Inf. Techn. (BMIT) Res. Group, School of Inf. Techn., Univ. of Sydney, Australia,Centre for Multimedia Signal Processing (CMSP), Dept. of Electronic, & Inf. Eng., Hon ...
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

Automated segmentation of brain MR images into gray matter, white matter and cerebrospinal fluid (CSF) has been extensively studied with many algorithms being proposed. However, most of those algorithms suffer from limited accuracy, due to the presence of intrinsic noise, low contrast and intensity inhomogeneity (INU) in MR images. In this paper, we propose the local Gaussian distribution fitting based fuzzy c-means (LGDFFCM) algorithm for automated and accurate brain MR image segmentation. In this algorithm, an energy function is defined by using the kernel function to characterize the fitting of local Gaussian distributions to the local image data within the neighborhood of each pixel. A new local scale computing method is developed to estimate the variances of local Gaussian distributions. We compared our algorithm to several state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed LGDFFCM algorithm can substantially reduce the impact of by noise, low contrast and INU, and produce satisfying segmentation of brain MR images.