Open AccessNon‐local‐based spatially constrained hierarchical fuzzy C ‐means method for brain magnetic resonance imaging segmentation
Author(s) -
Chen Yunjie,
Li Jian,
Zhang Hui,
Zheng Yuhui,
Jeon Byeungwoo,
Wu Qingming Jonathan
Publication year - 2016
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0271
Subject(s) - robustness (evolution) , segmentation , fuzzy logic , smoothing , artificial intelligence , computer science , image segmentation , gaussian , algorithm , pattern recognition (psychology) , cluster analysis , basis function , mathematics , computer vision , physics , biochemistry , chemistry , quantum mechanics , gene , mathematical analysis
Owing to the existence of noise and intensity inhomogeneity in brain magnetic resonance (MR) images, the existing segmentation algorithms are hard to find satisfied results. In this study, the authors propose an improved fuzzy C ‐mean clustering method (FCM) to obtain more accurate results. First, the authors modify the traditional regularisation smoothing term by using the non‐local information to reduce the effect of the noise. Second, inspired by the mechanism of the Gaussian mixture model, the distance function of FCM is defined by using the form of certain exponential function consisting of not only the distance but also the covariance and the prior probability to improve the robustness. Meanwhile, the bias field is modelled by using orthogonal basis functions to reduce the effect of intensity inhomogeneity. Finally, they use the hierarchical strategy to construct a more flexibility function, which considers the improved distance function itself as a sub‐FCM, to make the method more robust and accurate. Compared with the state‐of‐the‐art methods, experiment results based on synthetic and real MR images demonstrate its accuracy and robustness.