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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pham, D., Prince, J.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag. 18, 737–752 (1999)
Ahmed, M., Yamany, S., Mohamed, N., Farag, A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imag. 21, 193–199 (2002)
Liew, A., Yan, H.: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imag. 22, 1063–1075 (2003)
Chen, S.C., Zhang, D.Q.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Systems Man Cybernet. 34, 1907–1916 (2004)
Yang, M.S., Tsai, H.S.: A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recognition Letters 29, 1713–1725 (2008)
Liao, L., Lin, T., Li, B.: MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recognition Letters 29, 1580–1588 (2008)
Wang, L., He, L., Mishra, A., Li, C.: Active contours driven by local Gaussian distribution fitting energy. Signal Processing 89, 2435–2447 (2009)
Li, C., Xu, C., Anderson, A., Gore, J.: MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 288–299. Springer, Heidelberg (2009)
Saha, P.K., Udupa, J.K.: Optimum image thresholding via class uncertainty and region homogeneity. IEEE Trans. Pattern Anal. Mach. Intell. 23, 689–706 (2001)
Madabhushi, A., Udupa, J.K., Souza, A.: Generalized scale: theory, algorithms, and application to image inhomogeneity correction. Computer Vision and Image Understanding 21, 101–121 (2006)
Katkovnik, V., Shmulevich, I.: Nonparametric density estimation with adaptive varying window size. In: Image and Signal Processing for Remote Sensing VI, vol. 4170, pp. 141–150 (2001)
Brainweb, Simulated Brain Database, http://www.bic.mni.mcgill.ca/brainweb/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ji, Z., Xia, Y., Sun, Q., Xia, D., Feng, D.D. (2012). Local Gaussian Distribution Fitting Based FCM Algorithm for Brain MR Image Segmentation. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-31919-8_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
eBook Packages: Computer ScienceComputer Science (R0)