TY - GEN
T1 - Local Gaussian distribution fitting based FCM algorithm for brain MR image segmentation
AU - Ji, Zexuan
AU - Xia, Yong
AU - Sun, Quansen
AU - Xia, Deshen
AU - Feng, David Dagan
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - adaptive local scale
KW - local Gaussian distribution fitting
KW - MR image segmentation
UR - http://www.scopus.com/inward/record.url?scp=84865808653&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31919-8_41
DO - 10.1007/978-3-642-31919-8_41
M3 - 会议稿件
AN - SCOPUS:84865808653
SN - 9783642319181
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 318
EP - 325
BT - Intelligent Science and Intelligent Data Engineering - Second Sino-Foreign-Interchange Workshop, IScIDE 2011, Revised Selected Papers
T2 - 2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011
Y2 - 23 October 2011 through 25 October 2011
ER -