An improved level set method for brain MR images segmentation and bias correction

Chen, Yunjie; Zhang, Jianwei; Macione, James

doi:10.1016/j.compmedimag.2009.04.009

Cited by 53 publications

(36 citation statements)

References 27 publications

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“…In order to estimate the bias, this method models the image intensity as a multiple of the true image intensities and the component that accounts for the intensity non-uniformity. Chen et al (2009Chen et al ( , 2011 applied Gaussian distributions to model local intensity variations for bias correction and segmentation. Scherrer et al (2009) and Tohka et al (2010) proposed a different kind of local image modeling and segmentation in order to overcome intensity non-uniformity.…”

Section: Introductionmentioning

confidence: 99%

Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

Shahvaran

Kazemi

Helfroush

et al. 2012

Journal of Neuroscience Methods

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Section: Introductionmentioning

confidence: 99%

Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

Shahvaran

Kazemi

Helfroush

et al. 2012

Journal of Neuroscience Methods

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“…Wavelet theory provides a powerful framework to decompose images into different scales and orientations, and has been used in medical image processing [13]. The discrete wavelet transform (DWT) can provide multiple features of brain MR images [12].…”

Section: Introductionmentioning

confidence: 99%

Brain MR Image Segmentation with Spatial Constrained K-mean Algorithm and Dual-Tree Complex Wavelet Transform

Zhang

Jiang²,

Wang

et al. 2014

J Med Syst

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In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-mean algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-mean algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art algorithms.

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“…Recently, some papers have presented level set based methods to segment images meanwhile estimating the bias field [20,30] by using a weighted K-means clustering objective function for image intensities in a neighbourhood around each pixel, with the cluster centres having a multiplicative factor that estimates the bias within the neighbourhood. These methods only used the local mean information, which means the methods were regarded hard to segment objects with the same mean and different variances.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

Wang

et al. 2012

IET Computer Vision

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Intensity inhomogeneities cause considerable difficulties in the quantitative analysis of magnetic resonanceimages (MRIs). Consequently, intensity inhomogeneities estimation is a necessary step before quantitative analysis of MR data can be undertaken. This study proposes a new energy minimisation framework for simultaneous estimation of the intensity inhomogeneities and segmentation. The method was formulated by modifying the objective function of the standard fuzzy cmeans algorithm to compensate for intensity inhomogeneities by using basis functions and to compensate for noise by using improved non-local information. The energy function depends on the coefficients of the basis functions, the membership ratios, the centroid of the tissues and an improved non-local information in the image. Intensity inhomogeneities estimation and image segmentation are simultaneously achieved by calculating the result of minimising this energy. The non-local framework has been widely used to provide non-local information; however, the traditional framework only considers the neighbouring patch information, which will lose information of the corner and end points. This study presents an improved non-local framework, which can contain the corner and end points region information. Experimental results on both real MRIs and simulated MR data show that the authors method can obtain more accurate results when segmenting images with bias field and noise.

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An improved level set method for brain MR images segmentation and bias correction

Cited by 53 publications

References 27 publications

Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

Brain MR Image Segmentation with Spatial Constrained K-mean Algorithm and Dual-Tree Complex Wavelet Transform

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

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