Brain magnetic resonance image segmentation based on an adapted non-local fuzzy c-means method

Chen, Y.; Zhang, J.; Wang, S.; Zheng, Yinqiang

doi:10.1049/iet-cvi.2011.0263

Cited by 18 publications

(7 citation statements)

References 40 publications

(61 reference statements)

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“…Remark 1: Although the local patch information has been used to improve segmentation methods to reduce the effect of the noise; however, most of the methods use isotropic neighbor patch information, which makes them easily lose details [ 51 ]. Our method can preserve more detail information by using anisotropic neighbor patch information.…”

Section: Methodsmentioning

confidence: 99%

Brain MR image segmentation based on an improved active contour model

Meng

Chen

et al. 2017

PLoS ONE

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It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.

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Section: Methodsmentioning

confidence: 99%

Brain MR image segmentation based on an improved active contour model

Meng

Chen

et al. 2017

PLoS ONE

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“…Motivated by the use of non‐local information in NL_R_FCM [5] and the constructions of factor in RSCFCM [6], we propose an NLSCHFCM algorithm for brain MR image segmentation by introducing a novel non‐local‐based factor

{NLF}_{i, k}^{t} = exp (][, \frac{β}{2 N_{i}} \sum_{n \in \partial_{i}} W_{i, n} (z_{n, k}^{t} + π_{n, k}^{t}))

where W i , n is the weighted parameter calculated by using (4). Therefore, we proposed an improved fuzzy clustering‐type objective function based on the novel factor NLF i , k

J_{NLSCFCM} = \sum_{i = 1}^{N} \sum_{k = 1}^{K} u_{i, k}^{m} false(- log false(π_{i, k} ϕ false(x_{i} falsefalse| θ_{k} false) false) + \sum_{i = 1}^{N} \sum_{k = 1}^{K} {NLF}_{i, k}^{t} log false(π_{i, k} false)

In [27, 30], the bias field is reconstructed by using the liner combination of basis functions and can be written as

B_{i} = \sum_{l = 1}^{L} q_{l} φ_{l} ()(, i) = Q^{normalT} normalΨ ()(, i)

where q l ∈ R , l = 1, …, L , are the combination coefficients. φ l is the orthogonal basis function and satisfies:

\int_{normalΩ} φ_{i} (x) φ_{j} (x) normald x = δ_{i, j}

, δ i , j = 1 for i = j and δ i , j = 0 for i ≠ j .…”

Section: Non‐local‐based Spatially Constrained Fuzzy C‐means (Nlscfcm)mentioning

confidence: 99%

Non‐local‐based spatially constrained hierarchical fuzzy C ‐means method for brain magnetic resonance imaging segmentation

Chen

Zhang

et al. 2016

IET Image Processing

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“…This method needs to choose different parameters of the regular energy term when segmenting different images. More recently, along the same line, the fuzzy local [28] and non-local [13,22,34] information cmeans algorithms have been proposed.…”

Section: Related Workmentioning

confidence: 99%

Tumor segmentation in brain MRI using a fuzzy approach with class center priors

El-Melegy

Mokhtar

2014

J Image Video Proc

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This paper proposes a new fuzzy approach for the automatic segmentation of normal and pathological brain magnetic resonance imaging (MRI) volumetric datasets. The proposed approach reformulates the popular fuzzy c-means (FCM) algorithm to take into account any available information about the class center. The uncertainty in this information is also modeled. This information serves to regularize the clusters produced by the FCM algorithm thus boosting its performance under noisy and unexpected data acquisition conditions. In addition, it also speeds up the convergence process of the algorithm. Experiments using simulated and real, both normal and pathological, MRI volumes of the human brain show that the proposed approach has considerable better segmentation accuracy, robustness against noise, and faster response compared with several well-known fuzzy and non-fuzzy techniques reported in the literature.

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Brain magnetic resonance image segmentation based on an adapted non-local fuzzy c-means method

Cited by 18 publications

References 40 publications

Brain MR image segmentation based on an improved active contour model

Brain MR image segmentation based on an improved active contour model

Non‐local‐based spatially constrained hierarchical fuzzy C ‐means method for brain magnetic resonance imaging segmentation

Tumor segmentation in brain MRI using a fuzzy approach with class center priors

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