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.     

Automated atlas-based segmentation of NISSL-stained mouse brain sections using supervised learning

The problem of segmentation of mouse brain images into anatomical structures is an important stage of practically every analytical procedure for these images. The present study suggests a new approach to automated segmentation of anatomical structures in the images of NISSL-stained histological sections of mouse brain. The segmentation algorithm is based on the method of supervised learning using the existing anatomical labeling of the corresponding sections from a specialized mouse brain atlas. A mouse brain section to be segmented into anatomical structures is preliminarily associated with a section from the mouse brain atlas displaying the maximum similarity. The image of this section is then preprocessed in order to enhance its quality and to make it as close to the corresponding atlas image as possible. An efficient algorithm of luminance equalization, an extension of the well-known Retinex algorithm is proposed. A random forest is trained on pixel feature vectors constructed based on the atlas section images and the corresponding class labels associated with anatomical structures extracted from the atlas anatomical labeling. The trained classifier is then applied to classify pixels of an experimental section into anatomical structures. A new combination of features based on superpixels and location priors is suggested. Accuracy of the obtained result is increased by using Markov random field. Procedures of luminance equalization and subsequent segmentation into anatomical structures have been tested on real experimental sections.     

基于线性化核标签融合的脑MR图像分割方法

深层脑结构的形态变化和神经退行性疾病相关, 对脑MR图像中的深层脑结构分割有助于分析各结构的形态变化.多图谱融合方法利用图谱图像中的先验信息, 为脑结构分割提供了一种有效的方法.大部分现有多图谱融合方法仅以灰度值作为特征, 然而深层脑结构灰度分布之间重叠的部分较多, 且边缘不明显.为克服上述问题, 本文提出一种基于线性化核多图谱融合的脑MR图像分割方法.首先, 结合纹理与灰度双重特征, 形成增强特征用于更好地表达脑结构信息.其次, 引入核方法, 通过高维映射捕获原始空间中特征的非线性结构, 增强数据间的判别性和线性相似性.最后, 利用Nystr?m方法, 对高维核矩阵进行估计, 通过特征值分解计算虚样本, 并在核标签融合过程中利用虚样本替代高维样本, 大大降低了核标签融合的计算复杂度.在三个公开数据集上的实验结果表明, 本文方法在较少的时间消耗内, 提高了分割精度.     

基于最优模板选择和水平集的图谱分割算法

针对当前的图谱分割方法,在一定能高程度上缩短了计算时间,但是对于要求较高的应用软件来说,在精确度和平滑度上不能得到理想的分割结果.提出了一种最优模板选择和水平集的图谱分割算法.方法一方面使用正规化的交互信息获得"最优"模板.另一方面在图谱分割中提出基于水平集图谱分割方法,在基于光强度的配准算法的类型中引入附加约束条件,对约束条件可提高轮廓的平滑度,同时使引入的先验信息(如亮度分布或被分割物体可采纳的形状)更加局部化.实验结果表明,方法比传统的图谱分割方法有更高配准的精确度和平滑度.     

胎儿脑磁共振图像分割研究进展

医学影像是产前筛查、诊断、治疗引导和评估的重要工具,能有效避免胎儿脑的发育异常。近年来,磁共振成像在产前诊断中愈加重要,而实现自动、定量、精确地分析胎儿脑磁共振图像依赖于可靠的图像分割。因此,胎儿脑磁共振图像分割具有十分重要的临床意义与研究价值。由于胎儿图像中存在组织器官多、图像质量差及结构变化快等问题,胎儿脑磁共振图像的分割面临着巨大的困难与挑战。目前,尚未有文献对该领域的方法进行系统性的总结和分析,尤其是基于深度学习的方法。本文针对胎儿脑磁共振图像分割方法进行综述,首先,对胎儿脑磁共振图像的主要公开图谱/数据集进行详细说明;接着,对脑实质提取、组织分割和病灶分割方法进行全面的分类与分析;最后,对胎儿脑磁共振图像分割面临的挑战及未来的研究方向进行总结与展望。     

基于脑图谱和模糊聚类的磁共振图像分割标注

利用TT Atlas中丰富的结构信息,文章提出了一种自动分割脑MRI(magnetic resonance image)图像的方法.这种方法可分为两步.首先,将MRI图像和TT Atlas配准,通过图像和医学图谱的匹配,利用图谱中结构信息的先验知识,就可以对图像作初步的分割标注.然后,利用这个预分割的模板对MRI图像进行模糊聚类分割,从而提高分割的精度.为了自动地将预模板中的结构信息用于分割,文章还提出了一种引入形状因子的FCM聚类算法.除了在匹配时需要手工定出一些点之外,该方法基本上是自动的.     

基于两步图谱选择的脑MR图像分割

针对传统的基于多图谱的医学图像分割过程中的相似度加权融合的方法没有考虑图谱集的干扰性和冗余性的不足, 提出一种基于两步图谱选择策略的脑MR图像分割方法. 该方法首先采用一种基于最小角回归的方法进行图谱粗选择, 其次则采用基于豪斯多夫距离的以目标为导向的图谱精选择. 粗选择方法可以在总体上来寻找和目标图像较为相似的图谱,...     

Image registration by local histogram matching

We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images, DTI brain images, CT pelvis images, and MR mouse images.     

Target recognition by texture segmentation algorithm

In order to improve the performance of image segmentation, this paper presented a gray level jump segmentation algorithm, which defined the direction of the texture, simultaneously, calculated the width of ridge line, gave the distance characteristics between textures, and established the mathematical model of the texture border, accordingly presented a new texture segmentation algorithm and compared with other texture segmentation algorithms. The simulation results show that the segmentation algorithm has some advantages to texture segmentation, such as has higher segmentation precision, faster segmentation speed, stronger anti-noise capability, less lost information of target, and so on. The segmented regions hardly contain other texture regions and background region. Moreover, this paper extracted the characteristic points and characteristic parameters in various segmented regions for texture image to obtain the characteristic vector, compared the characteristic vector with the standard template vectors, and identified the type of target in a range of threshold value. Experimental results show that the proposed target recognition approach has higher recognition rate and faster recognition speed than the existing target recognition approaches. Advancements in image processing through the study of texture segmentation are not only applicable to image fields, but also are of important theoretical value to target recognition. These researches in this paper will play an important role in a theoretical reference and practical significance to the development of all target recognition departments based on image system such as the aerospace, public security, road traffic, and so on.     

MRI脑肿瘤图像分割研究进展及挑战

脑肿瘤分割是医学图像处理中的一项重要内容,其目的是辅助医生做出准确的诊断和治疗,在临床脑部医学领域具有重要的实用价值。核磁共振成像（MRI）是临床医生研究脑部组织结构的主要影像学工具,为了使更多研究者对MRI脑肿瘤图像分割理论及其发展进行探索,本文对该领域研究现状进行综述。首先总结了用于MRI脑肿瘤图像分割的方法,并对现有方法进行了分类,即分为监督分割和非监督分割;然后重点综述了基于深度学习的脑肿瘤分割方法,在研究其关键技术基础上归纳了优化策略;最后介绍了脑肿瘤分割（BraTS）挑战,并结合挑战中所用方法展望了脑肿瘤分割领域未来的发展趋势。MRI脑肿瘤图像分割领域的研究已经取得了一些显著进展,尤其是深度学习的发展为该领域的研究提供了新的思路。但由于脑肿瘤在大小、形状和位置方面的高度变化,以及脑肿瘤图像数据有限且类别不平衡等问题,使得脑肿瘤图像分割仍是一个极具挑战的课题。由于分割过程缺乏可解释性和透明性,如何将全自动分割方法应用于临床试验,还需要进行深入研究。     

3D anatomical shape atlas construction using mesh quality preserved deformable models

3D anatomical shape atlas construction has been extensively studied in medical image analysis research, owing to its importance in model-based image segmentation, longitudinal studies and populational statistical analysis, etc. Among multiple steps of 3D shape atlas construction, establishing anatomical correspondences across subjects, i.e., surface registration, is probably the most critical but challenging one. Adaptive focus deformable model (AFDM) [1] was proposed to tackle this problem by exploiting cross-scale geometry characteristics of 3D anatomy surfaces. Although the effectiveness of AFDM has been proved in various studies, its performance is highly dependent on the quality of 3D surface meshes, which often degrades along with the iterations of deformable surface registration (the process of correspondence matching). In this paper, we propose a new framework for 3D anatomical shape atlas construction. Our method aims to robustly establish correspondences across different subjects and simultaneously generate high-quality surface meshes without removing shape details. Mathematically, a new energy term is embedded into the original energy function of AFDM to preserve surface mesh qualities during deformable surface matching. More specifically, we employ the Laplacian representation to encode shape details and smoothness constraints. An expectation–maximization style algorithm is designed to optimize multiple energy terms alternatively until convergence. We demonstrate the performance of our method via a set of diverse applications, including a population of sparse cardiac MRI slices with 2D labels, 3D high resolution CT cardiac images and rodent brain MRIs with multiple structures. The constructed shape atlases exhibit good mesh qualities and preserve fine shape details. The constructed shape atlases can further benefit other research topics such as segmentation and statistical analysis.     

基于邻域信息的改进模糊c均值脑MRI分割

在脑图像分割中,噪声或异常值的干扰往往会使得图像的质量下降。而传统的模糊c均值算法存在一定的缺限,容易受初始值的影响,这给医生准确识别和提取脑组织带来很大的麻烦。针对这些问题,提出一种基于用马尔可夫模型构建的图像像素点邻域的改进模糊c均值图像分割方法。首先,用遗传算法（GA）确定初始的聚类中心;然后,改变目标函数的表达方式,通过在目标函数中添加修正项来改变隶属度矩阵的计算方式,并用约束系数对其来调节;最后,由马尔可夫随机域来表达邻域像素的标号信息,并利用马尔可夫随机场（MRF）的最大化条件概率来表示像素的邻域,增强了抗噪性。实验结果显示,该方法拥有较好的抗噪性,可以降低误分割率,在对脑图像分割时具备较高的分割精度。分割后的图像平均精度可达：JS（Jaccard Similarity）指标为82.76%,Dice指标为90.45%,Sensitivity指标为90.19%;同时,对脑图像边界处的分割更加清晰,分割后的图像更加接近于标准分割图像。     

Full anatomical labeling of magnetic resonance images of human brain by registration with multiple atlases

The problem of automatic segmentation of magnetic resonance (MR) images of human brain into anatomical structures is considered. Currently, the most popular segmentation algorithms are based on the registration (matching) of the input image with (to) an atlas—an image for which an expert labeling is known. Segmentation on the basis of registration with multiple atlases allows one to better take into account anatomical variability and thereby to compensate, to some extent, for the errors of matching to each individual atlas. In this work, a more efficient (in speed and memory) implementation is proposed of one of the best multiatlas label fusion algorithms in order to obtain a labeling of the input image. The algorithm is applied to the problem of segmentation of brain MR images into 43 anatomical regions with the use of the publicly available IBSR database, in contrast to the original work, where the authors provide test results for the problem of extraction of a single anatomical structure, the hippocampus.     

Toward objective evaluation of image segmentation algorithms

Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm evaluation through an example evaluation of some familiar algorithms - the mean-shift-based algorithm, an efficient graph-based segmentation algorithm, a hybrid algorithm that combines the strengths of both methods, and expectation maximization. Results are presented on the 300 images in the publicly available Berkeley segmentation data set     

基于粗糙集理论的图像分割智能决策方法

尽管如今已有多种图像分割算法,但是没有任何一种分割方法能够适用于所有的图像.为了使图像跟踪系统能根据图像特征自适应选取分割算法,给出了一种基于粗糙集理论的图像分割智能决策方法.该方法首先选取若干具代表性的分割算法构成算法库,并用它们对各种样本图像进行分割;然后利用从样本图像中提取出来的各种数值特征,并根据图像分割质量评价标准评判出各样本图像的最优分割算法,用其构成决策信息表;最后应用粗糙集理论来对决策信息表进行离散化处理和属性约简,以生成图像分割算法选取的决策规则.该决策方法解决了图像跟踪系统中分割算法选取的一系列难题.实验证明,该决策方法能比较有效地根据系统所处理图像的特征选取出算法库中最优的分割算法,并可满足车载图像跟踪系统的实时性要求.     

基于形态学重构运算的医学图像分割

医学图像分割是高层次医学图像理解和解释的前提条件,其目的是通过提取描述对象的特征,把感兴趣对象从周围环境中分离出来。文章在形态学基本运算的基础上,介绍了形态学重构运算和形态学图像分割的基本流程,最后用形态学重构运算对脑部MRI图像进行了分割,实验结果表明,这一方法可成功地将脑髓分割出来。     

Segmentation fusion based on neighboring information for MR brain images

In this paper, we study on how to boost image segmentation algorithms. First of all, a novel fusion scheme is proposed to combine different segmentations with mutual information to reduce misclassified pixels and obtain an accurate segmentation. As the class label of each pixel depends on the pixel’s gray level and neighbors’ labels, the fusion scheme takes both spatial and intensity information of pixels into account. Then, a detail thresholding segmentation case is designed using the proposed fusion scheme. In the case, the local Laplacian filter is used to get the smoothed version of original image. To accelerate segmentation, a discrete curve evolution based Otsu method is employed to segment the original image and its smoothed version to get two different segmentation maps. The fusion scheme is used to fuse the two maps to get the final segmentation result. Experiments on medical MR-T2 brain images are conducted to demonstrate the effectiveness of the proposed segmentation fusion method. The experimental results indicate that the proposed algorithm can improve segmentation accuracy and it is superior to other multilevel thresholding methods.     

基于色彩学习的彩色图象分割方法

图象分割是实现计算机图象识别与理解的基础，而彩色是进行图象分割的一个重要手段。本文给出了一种基色彩学习的彩色图象分割算法。     

Template-driven segmentation of confocal microscopy images

High quality 3D visualization of anatomic structures is necessary for many applications. The anatomic structures first need to be segmented. A variety of segmentation algorithms have been developed for this purpose. For confocal microscopy images, the noise introduced during the specimen preparation process, such as the procedure of penetration or staining, may cause images to be of low contrast in some regions. This property will make segmentation difficult. Also, the segmented structures may have rugged surfaces in 3D visualization. In this paper, we present a hybrid method that is suitable for segmentation of confocal microscopy images. A rough segmentation result is obtained from the atlas-based segmentation via affine registration. The boundaries of the segmentation result are close to the object boundaries, and are regarded as the initial contours of the active contour models. After convergence of the snake algorithm, the resulting contours in regions of low contrast are locally refined by parametric bicubic surfaces to alleviate the problem of incorrect convergence. The proposed method increases the accuracy of the snake algorithm because of better initial contours. Besides, it can provide smoother segmented results in 3D visualization.