Automated segmentation of brain MR images

A simple, robust and efficient image segmentation algorithm for classifying brain tissues from dual echo Magnetic Resonance (MR) images is presented. The algorithm consists of a sequence of adaptive histogram analysis, morphological operations and knowledge based rules to accurately classify various regions such as the brain matter and the cerebrospinal fluid, and detect if there are any abnormal regions. It can be completely automated and has been tested on over hundred images from several patient studies. Experimental results are provided.     

基于模糊马尔可夫场的脑部MR图像分割算法

在传统马尔可夫场模型的基础上,建立了模糊马尔可夫场模型。通过对模型的分析得出图像像素对不同类的隶属度计算公式,提出了一种高效、无监督的图像分割算法,从而实现了对脑部MR图像的精确分割。通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明新算法比传统的基于马尔可夫场的图像分割算法和模糊C-均值等图像分割算法有更精确的图像分割能力。     

A review of atlas-based segmentation for magnetic resonance brain images

Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.     

Adaptive pixon represented segmentation (APRS) for 3D MR brain images based on mean shift and Markov random fields

In this paper, we proposed an adaptive pixon represented segmentation (APRS) algorithm for 3D magnetic resonance (MR) brain images. Different from traditional method, an adaptive mean shift algorithm was adopted to adaptively smooth the query image and create a pixon-based image representation. Then K-means algorithm was employed to provide an initial segmentation by classifying the pixons in image into a predefined number of tissue classes. By using this segmentation as initialization, expectation-maximization (EM) iterations composed of bias correction, a priori digital brain atlas information, and Markov random field (MRF) segmentation were processed. Pixons were assigned with final labels when the algorithm converges. The adoption of bias correction and brain atlas made the current method more suitable for brain image segmentation than the previous pixon based segmentation algorithm. The proposed method was validated on both simulated normal brain images from BrainWeb and real brain images from the IBSR public dataset. Compared with some other popular MRI segmentation methods, the proposed method exhibited a higher degree of accuracy in segmenting both simulated and real 3D MRI brain data. The experimental results were numerically assessed using Dice and Tanimoto coefficients.     

Fast deformable matching of 3D images over multiscale nested subspaces. Application to atlas-based MRI segmentation

This paper presents a fast method to perform dense deformable matching of 3D images, applied to the registration of inter-subject brain MR images. To recover the complex morphological variations in neuroanatomy, the registration method uses a hierarchy of 3D deformations fields that are estimated, by minimizing a global energy function over a sequence of nested subspaces. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR images from different individuals. An application of the deformable image matching method to 3D atlas-based image segmentation is presented. This atlas-based segmentation is used at Strasbourg Hospital, in daily clinical applications, in order to extract regions of interest from 3D MR images of patients suffering from epilepsy.     

Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

As a result of noise and intensity non-uniformity,automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task.In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion.In the proposed method,fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures.The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements.The results of the proposed method are evaluated using Dice similarity and Accuracy indices.Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.     

Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network

Magnetic resonance imaging (MRI) is a non-invasive diagnostic tool very frequently used for brain imaging. The classification of MRI images of normal and pathological brain conditions pose a challenge from technological and clinical point of view, since MR imaging focuses on soft tissue anatomy and generates a large information set and these can act as a mirror reflecting the conditions of the brain. A new approach by integrating wavelet entropy based spider web plots and probabilistic neural network is proposed for the classification of MRI brain images. The two step method for classification uses (1) wavelet entropy based spider web plots for the feature extraction and (2) probabilistic neural network for the classification. The spider web plot is a geometric construction drawn using the entropy of the wavelet approximation components and the areas calculated are used as feature set for classification. Probabilistic neural network provides a general solution to the pattern classification problems and the classification accuracy is found to be 100%.     

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain.     

Multiscaled combination of MR and SPECT images in neuroimaging: A simplex method based variable-weight fusion

Single-photon emission computed tomography (SPECT) images alone are difficult to understand in diagnosis, since anatomical structures are absent from the data. Studies on combination attempt to locate functional changes of the SPECT image by the magnetic resonance (MR) image. Due to the low similarity between original images, fused results are always darkened, obscured or loss some crucial anatomical structures. This paper has solved these problems by the variable-weight matrix which is estimated by minimizing the cost function using the simplex method. Under the generalized intensity-hue-saturation (GIHS) framework, the multiscaled analysis is presented for a better detail preservation. Besides, interactive approaches are discussed for the gradual variation between original images and the control of detail performance. The similarity assessment evaluates several different methods on a normal brain atlas. Two clips show the interactive property of the proposed method, while two medical cases demonstrate its clinical values. We conclude that the proposed method is superior to traditional methods, when considering the definition and the information capacity of fused results.     

基于图像片马尔科夫随机场的脑MR图像分割算法

传统的高斯混合模型（Gaussian mixture model,GMM）算法在图像分割中未考虑像素的空间信息,导致其对于噪声十分敏感.马尔科 夫随机场（Markov random field,MRF）模型通过像素类别标记的Gibbs分布先验概率引入了图像的空间信息,能较好地分割含有噪声的图 像,然而MRF模型的分割结果容易出现过平滑现象.为了解决上述缺陷,提出了一种新的基于图像片权重方法的马 尔科夫随机场图像分割模型,对邻域内的不同图像片根据相似度赋予不同的权重,使其在克服噪声影响的同时能 保持图像细节信息.同时,采用KL距离引入先验概率与后验概率关于熵的惩罚项,并对该惩罚项进行平滑,得到 最终的分割结果.实验结果表明,算法具有较强的自适应性,能够有效克服噪声对于分割结果的影响,并获得较高的分割精度.     

乳腺磁共振图像中偏差场的校正

文中提出一种校正乳腺磁共振图像中光滑、缓慢变化的偏差场的方法。它在同态滤波的基础上,用B样条对初步估计的偏差场进行平滑,从而能更有效地估计磁共振图像中光滑、缓慢变化的偏差场。在有偏差场的模型图像和MR乳腺图像的校正中,此方法均取得了成功。     

Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty

Non-Gaussian triplet Markov random fields (TMF) model is suitable for dealing with multi-class segmentation of nonstationary and non-Gaussian synthetic aperture radar (SAR) images. However, the segmentation of SAR images utilizing this model still fails to resolve the misclassifications due to the inaccuracy of edge location. In this paper, we propose a new unsupervised multi-class segmentation algorithm by fusing the traditional energy function of TMF model with the principle of edge penalty. Through the introduction of the penalty function based on local edge strength information, the new energy function could prevent segment from smoothing across boundaries. Then we optimize the objective function that stems from the new energy function to obtain an iterative multi-region merging Bayesian maximum posterior mode (MPM) segmentation equation for the new segmentation algorithm. The effectiveness of the proposed algorithm is demonstrated by application to simulated data and real SAR images.     

Unsupervised image segmentation using a simple MRF model with a new implementation scheme

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture) and achieves an improvement over the traditional method.