一种基于ITK脑部MRI混合分割方法的设计与实现

背景:由于脑部MR图像中信息对比度不高,各种脑部组织的形状复杂等特点,分割方法的选择比较困难,单一的算法很难获得满意的分割结果。目的:针对脑部MRI的特点综合利用现有的算法开发和定制有效的分割应用算法。方法:根据邻域连接和Canny水平集分割算法的优缺点,结合图像特征,用邻域连接方法的分割结果作为Canny水平集分割算法的先验分割模型,借以确定出Canny算法的下限阈值,从而完成两种算法的混合分割。结果与结论:采用实验所用混合方法得到的白质和灰质的分割结果,经与专家手工分割结果对比,证明该方法取得了较好的分割效果,从而证明综合利用现有的算法,不仅避免了重复劳动,还能开发和定制出更加有效的分割应用算法,具备很好的应用潜力。     

基于图割与粗糙集的MRI脑部肿瘤图像检索方法

背景:基于内容的医学图像检索是一门涉及多领域的学科,由于各种医学图像的成像原理不同,产生的图像在颜色、纹理和形状等视觉特征方面存在差别,使得此方法的实现还存在许多需要解决的问题.目的:针对基于内容的医学图像检索中存在特征提取困难、检索时间长的问题,提出一种基于图割与粗糙集结合的相似图像检索方法.方法:为克服图割仅适用于较少象素的图像和倾向于小割集的缺陷,首先对图像进行聚类,然后构建图像的Gomory-Hu割树,按割值大小依次去掉值较小的边,提取出图像的特征子图并构建特征库.为实现快速检索,借助粗糙集对特征库中的特征进行约简,有效减少参与相似性比较的特征数量.并将此方法应用到MRI脑部肿瘤图像的检索.结果与结论:实验结果表明该方法能快速有效地检索出MRI脑部图像库中的肿瘤图像,检索的平均查准率为78.4%,平均查全率为62.9%.     

一种半自动分割脑部MRI中胼胝体的方法

针对胼胝体的图像特点以及实际应用要求,采用半自动方法对MRI中的胼胝体进行分割。首先采用基于Live-Wire的算法对胼胝体影像的起始层和终止层进行初始分割,然后利用基于距离变换的形状插值算法获取中间层的初始轮廓信息,对插值获得的初始轮廓采用Snake模型进行局部收缩,获得真实的胼胝体边界。对序列MRI脑影像中的胼胝体进行分割、重建、标定。实验结果与临床医师的使用反馈证明,本文提出的算法具有较高的灵活性与可信度,对胼胝体的分割精度与解剖统计信息相符,分割结果可满足临床需求。     

基于ITK、VTK和MFC的DICOM图像读写及显示

背景：DICOM文件不仅包含了图像本身的信息,同时在文件头中还携带了大量的医疗相关信息,这使得DICOM标准图像的正确读写和显示工作变得尤为重要。目的：有效地综合利用工具包和开发平台的优点,借以达到一个实现DICOM标准图像的正确读写和显示。方法：首先将ITK工具包、VTK工具包与MFC深度集成,然后在该集成环境下利用这两个工具包所提供的类和函数读写和显示DICOM文件。结果与结论：在ITK、VTK与MFC深度集成环境下,通过其所提供给用户的一个灵活、友好、实用的交互界面,采用文中所述具体的方法实现了DICOM图像文件的正确读写及显示,为三者集成环境下的软件开发做了一个初步的尝试,为在此基础之上三者在医学图像处理方面更多功能的实现打下良好的基础。     

基于图像分割的Tamura纹理特征算法的研究与实现

背景：ITK主要提供医学图像处理、分割与配准算法,但其缺少可视化的功能,缺乏灵活实用的用户界面,VTK提供了丰富的医学影像处理与分析工具,具有强大的图形处理和可视化功能。目的：利用以前的确诊病例和医生的诊断经验以及患者的相关病史,对确诊的医学影像资源进行管理,归档,并检索,以减少人工干预,提高图像的查全率和查准率。方法：以视觉感知机制为基础,在ITK平台上进行图像平滑去噪和分割的预处理过程,利用Tamura算法完成纹理特征提取,最后通过实验采集、计算数据,完成对比分析。结果与结论：基于图像分割的Tamura纹理特征算法在基于图像纹理检索应用上便于相似性度量,进而可提高检索的准确率。     

基于自动策略的脑图像分割

目的脑图像分割在外科手术规划和脑疾病诊断等方面都起着极为重要的作用,建立脑图像分割的自动策略成为一种需要。方法通过各向异性滤波,统计阈值分割,数学形态学滤波,和基于模糊连接算法对脑图像进行自动分割。结果实验表明这种分割策略能取得良好的分割结果。结论本文提出的算法可以有效地完成脑图像的自动分割工作。     

基于一种改进的相位校正算法消除MRI运动伪影

背景：近年来,MRI由于具有高的空间分辨率和软组织对比度,在临床上的运用越来越广泛。但是其成像时间较长,所以容易受到患者身体运动的影响,产生运动伪影。目的：去除MRI图像成像时产生的伪影,改善图像质量。方法：使用改进的相位矫正算法,并结合水平集算法去除图像伪影。去除伪影后使用模糊增强改善处理后图像的质量。结果与结论：实验证明使用改进的相位矫正算法得到的图像比使用原始的相位矫正算法得到的图像效果更加理想。     

基于一种改进的相位校正算法消除MRI运动伪影

背景:近年来,MRI由于具有高的空间分辨率和软组织对比度,在临床上的运用越来越广泛.但是其成像时间较长,所以容易受到患者身体运动的影响,产生运动伪影.目的:去除MRI图像成像时产生的伪影,改善图像质量.方法:使用改进的相位矫正算法,并结合水平集算法去除图像伪影.去除伪影后使用模糊增强改善处理后图像的质量.结果与结论:实验证明使用改进的相位矫正算法得到的图像比使用原始的相位矫正算法得到的图像效果更加理想.     

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景：在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的：将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法：针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论：实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

点对称距离模糊C均值聚类算法在脑部MRI图像分割中的应用

背景:在传统的图像分割方法中,模糊C均值聚类算法应用十分广泛。目的:将改进的模糊C均值聚类算法应用到MRI图像的分割中,提高MRI图像分割的准确度。方法:针对传统的基于Minkowski距离的模糊C均值聚类算法,提出了基于点对称距离的模糊C均值聚类算法,并将其运用到了脑部MRI图像分割中。结果与结论:实验结果表明,与模糊C均值聚类算法相比,点对称距离的模糊C均值聚类算法有明显的优势。     

基于ITK和VTK方法的超声血管图像三维重建

医学图像三维重建是利用二维图像序列重建出三维模型,提供直观的视觉信息,供医务工作者参考.大多数重建都是利用CT图像序列进行重建,对超声图像的重建研究很少.文章首先介绍ITK(Insight Segmentation and Registration Toolkit)和VTK(The Visualization Toolkit),接着利用ITK和VTK进行了超声图像的三维重建,最后给出了实验结果,重建结果表明,利用ITK、VTK和改进的Herman插值法,超声血管图像可以获得很好的重建效果.     

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.     

基于期望值最大化方法的磁共振图像人脑组织分割

目的利用期望值最大化方法进行磁共振图像的人脑组织分割。方法在分析当前常用的医学图像分割方法的基础上,提出一种基于统计理论的期望值最大化分割方法,并给出了相应的理论算法模型和实现步骤,最后用Visual C 6.0编程,并对磁共振大脑图像进行实验,并与应用SPM软件对同一幅图像的分割结果进行分析比较。结果本文分割方法与SPM软件的分割结果非常接近,大脑灰质、白质、脑脊液等组织之间边界清晰,总体不确定性较小。结论本文分割方法切实可行,分割效果较好,为进一步的磁共振图像分析和疾病研究提供了一种有效工具。     

Expert knowledge-guided segmentation system for brain MRI

We describe an automated 3-D segmentation system for in vivo brain magnetic resonance images (MRI). Our segmentation method combines a variety of filtering, segmentation, and registration techniques and makes maximum use of the available a priori biomedical expertise, both in an implicit and an explicit form. We approach the issue of boundary finding as a process of fitting a group of deformable templates (simplex mesh surfaces) to the contours of the target structures. These templates evolve in parallel, supervised by a series of rules derived from analyzing the template's dynamics and from medical experience. The templates are also constrained by knowledge on the expected textural and shape properties of the target structures. We apply our system to segment four brain structures (corpus callosum, ventricles, hippocampus, and caudate nuclei) and discuss its robustness to imaging characteristics and acquisition noise.     

A prior feature SVM-MRF based method for mouse brain segmentation

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~ 87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~ 50% to ~ 80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders.     

Infant brain probability templates for MRI segmentation and normalization

Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.     

一种基于人脑三维模型分割断面图像上脑沟、回的方法与实现

目的 借助人脑三维模型实现二维断面图像上大脑沟、回的分割。方法 首先在三维脑模型上以勾勒轮廓的方式界定不同脑沟、脑回区域,然后映射到断面相应区域上,进行区域内颜色填充,达到分割目的;并采用Visual C++ 6.0结合可视化类库工具包搭建脑沟、回分割平台,予以实现。结果 准确有效地分割出了序列断面图像上的右脑中央前回和中央后回。结论 此方法为获取完整、连续的脑沟、脑回断面解剖图谱提供了一种简单可行的实现手段,对于丰富数字化脑图谱及促进脑部功能与疾病诊断定位相关研究有重要意义。     

A unified framework for cross-modality multi-atlas segmentation of brain MRI

Multi-atlas label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. A standard label fusion algorithm relies on independently computed pairwise registrations between individual atlases and the (target) image to be segmented. These registrations are then used to propagate the atlas labels to the target space and fuse them into a single final segmentation. Such label fusion schemes commonly rely on the similarity between intensity values of the atlases and target scan, which is often problematic in medical imaging – in particular, when the atlases and target images are obtained via different sensor types or imaging protocols.In this paper, we present a generative probabilistic model that yields an algorithm for solving the atlas-to-target registrations and label fusion steps simultaneously. The proposed model does not directly rely on the similarity of image intensities. Instead, it exploits the consistency of voxel intensities within the target scan to drive the registration and label fusion, hence the atlases and target image can be of different modalities. Furthermore, the framework models the joint warp of all the atlases, introducing interdependence between the registrations.We use variational expectation maximization and the Demons registration framework in order to efficiently identify the most probable segmentation and registrations. We use two sets of experiments to illustrate the approach, where proton density (PD) MRI atlases are used to segment T1-weighted brain scans and vice versa. Our results clearly demonstrate the accuracy gain due to exploiting within-target intensity consistency and integrating registration into label fusion.