Yunjie Chen

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

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.

Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy

This paper presents a variational level set approach in a multi-phase formulation to segmentation of brain magnetic resonance (MR) images with intensity inhomogeneity. In our model, the local image intensities are characterized by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with level set functions and local means and variances as variables. The means and variances of local intensities are considered as spatially varying functions. Therefore, our method is able to deal with intensity inhomogeneity without inhomogeneity correction. Our method has been applied to 3T and 7T MR images with promising results.

Image segmentation and bias correction via an improved level set method

Intensity inhomogeneity causes considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus bias field estimation is a necessary pre-processing step before quantitative analysis of MR data. This paper presents a variational level set approach for bias correction and segmentation for images with intensity inhomogeneities. Our method is based on the observation that local intensity variations in relatively smaller regions are separable, despite the inseparability of the whole image. In the beginning we define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. Generally the local intensity variations are described by the Gaussian distributions with different means and variances. In this work the objective functions are integrated over the entire domain with local Gaussian distribution of fitting energy, ultimately analyzing the data with a level set framework. Our method is able to capture bias of quite general profiles. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.

Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model

Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.

An improved variational level set method for MR image segmentation and bias field correction

In this paper, we propose an improved variational level set approach to correct the bias and to segment the magnetic resonance (MR) images with inhomogeneous intensity. First, we use a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. By using the information of the local variance in this descriptor, our method is able to obtain accurate segmentation results. Furthermore, we extend this method to three-phase level set formulation for brain MR image segmentation and bias field correction. By using this three-phase level set function to replace the four-phase level set function, we can reduce the number of convolution operations in each iteration and improve the efficiency. Compared with other approaches, this algorithm demonstrates a superior performance.

An anisotropic images segmentation and bias correction method.

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field correction is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents an anisotropic approach to bias correction and segmentation for images with intensity inhomogeneities and noise. Intensity-based methods are usually applied to estimate the bias field; however, most of them only concern the intensity information. When the images have noise or slender topological objects, these methods cannot obtain accurate results or bias fields. We use structure information to construct an anisotropic Gibbs field and combine the anisotropic Gibbs field with the Bayesian framework to segment images while estimating the bias fields. Our method is able to capture bias of quite general profiles. Moreover, it is robust to noise and slender topological objects. The proposed method has been used for images of various modalities with promising results.

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

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.

An improved anisotropic hierarchical fuzzy c-means method based on multivariate student t-distribution for brain MRI segmentation

Accurate segmentation for magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of intensity inhomogeneity (also named as bias field) and noise in the MR images, many segmentation methods are suffer from limited robustness and hard to find accurate results. This paper proposes an improved anisotropic multivariate student t-distribution based hierarchical fuzzy c-means method (IAMTHFCM). Firstly, improved anisotropic spatial information, defined in the neighborhoods of each pixel, is proposed to overcome the effect of the noise and preserve more detail information, especially for points with less repetitive patterns, such as corner and end points. Secondly, the improved anisotropic spatially information is utilized into a negative multivariate student t-distribution based log-posterior as the dissimilarity function to improve the robustness and accuracy. Thirdly, we use the hierarchical strategy to construct a more flexible objective function by considering the improved dissimilarity function itself as a sub-FCM, to make the method more robust and accurate to outliers and weak edges. Finally, the intensity inhomogeneities is modeled as a linear combination of a set of orthogonal basis functions, and parameterized by the coefficients of the orthogonal basis functions. Then the objective function is integrated with the bias field estimation and makes the proposed method can estimate the bias field meanwhile segmenting images. The segmentation and the bias field estimation can obtain benefit from each other. Our statistical results on both synthetic and clinical images show that the proposed method can overcome the difficulties caused by noise and bias fields and obtain more accurate results. Reducing the effect of noise and preserving more details.Using multivariate t-distribution to improve the robustness.Using hierarchical FCM to reduce the effect of outliers.The method can obtain more accurate segmented results and estimated bias field.

Chinese Visible Human Brain Image Segmentation

The Visible Human data set provides researchers with digital cross-sections of the human body. Many institutions use the Visible Human for research and teaching purposes. In this paper, we would like to share our experience in analyse the Chinese Visible Human (CVH) Brain images. We introduce some methods, such as skull stripping, de-noise, etc, to segment the Brain images and get better results. These results can be used to develop virtual medical applications in virtual anatomy.

A New Fast Brain Skull Stripping Method

The segmentation of brain tissue from non-brain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. In this paper, we propose a fast automatic skull-stripping method. The proposed method is based on an adaptive gauss mixture model and a 3D Mathematical Morphology method. The adaptive gauss mixture model classifies the brain tissues, meanwhile estimates the bias field. The new 3D Mathematical Morphology method can skull stripping other tissues efficiently and accurately. Comparisons with two existing methods, the brain extraction tool (BET) and the brain surface extractor (BSE), show the promising results of our method in terms of robustness and accuracy. Keywords-Skull stripping, denoising, table method