Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a “context penalty” that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method’s robustness to noise and inaccurate segmentations.     

Endocardial Border Detection in Cardiac Magnetic Resonance Images Using Level Set Method

Segmentation of the left ventricle in MRI images is a task with important diagnostic power. Currently, the evaluation of cardiac function involves the global measurement of volumes and ejection fraction. This evaluation requires the segmentation of the left ventricle contour. In this paper, we propose a new method for automatic detection of the endocardial border in cardiac magnetic resonance images, by using a level set segmentation-based approach. To initialize this level set segmentation algorithm, we propose to threshold the original image and to use the binary image obtained as initial mask for the level set segmentation method. For the localization of the left ventricular cavity, used to pose the initial binary mask, we propose an automatic approach to detect this spatial position by the evaluation of a metric indicating object’s roundness. The segmentation process starts by the initialization of the level set algorithm and ended up through a level set segmentation. The validation process is achieved by comparing the segmentation results, obtained by the automated proposed segmentation process, to manual contours traced by tow experts. The database used was containing one automated and two manual segmentations for each sequence of images. This comparison showed good results with an overall average similarity area of 97.89%.     

Automatic Cardiac Segmentation Using Semantic Information from Random Forests

We propose a fully automated method for segmenting the cardiac right ventricle (RV) from magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into superpixels and each superpixel is analyzed to detect the presence of RV regions using random forest (RF) classifiers. The superpixels containing RV regions constitute the region of interest (ROI) which is used to segment the actual RV. Probability maps are generated for each ROI pixel using a second set of RF classifiers which give the probabilities of each pixel belonging to RV or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low-level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that compared to conventional method our algorithm achieves superior performance due to the inclusion of semantic knowledge and context information.     

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.     

Automatic Prospective Registration of High-Resolution Trabecular Bone Images of the Tibia

Magnetic Resonance Imaging (MRI) longitudinal studies conducted to assess changes in tibia bone quality impose strict requirements on the reproducibility of the prescribed region acquired. Registration, the process of aligning two images, is commonly performed on the images after acquisition. However, techniques to improve image registration precision by adjusting scanning parameters prospectively, prior to image acquisition, would be preferred. We have adapted an automatic prospective mutual information based registration algorithm to a MRI longitudinal study of trabecular bone of the tibia and compared it to a post-scan manual registration. Qualitatively, image alignment due to the prospective registration is shown in 2D subtraction images and 3D surface renderings. Quantitatively, the registration performance is demonstrated by calculating the sum of the squares of the subtraction images. Results show that the sum of the squares is lower for the follow up images with prospective registration by an average of 19.37% ± 0.07 compared to follow up images with post-scan manual registration. Our study found no significant difference between the trabecular bone structure parameters calculated from the post-scan manual registration and the prospective registration images (p > 0.05). All coefficient of variation values for all trabecular bone structure parameters were within a 2–4.5% range which are within values previously reported in the literature. Results suggest that this algorithm is robust enough to be used in different musculoskeletal imaging applications including the hip as well as the tibia.     

心脏灌注磁共振成像的图像配准

医学时序图像的配准是医学图像分析诊断的基础，也是图像融合等图像处理需要先行解决的问题。本研究提出了一种新的基于图像分割和相关法的算法，用于准确、快速实现心脏灌注核磁共振成像(MRI)图像的配准。该方法以图像相关系数为目标函数，利用迭代运算确定心脏图像的水平位移、垂直位移和旋转角度等参数。该算法优点在于充分考虑了心肌灌注图像的特殊性以及被测对象的身体物理运动、生理运动。结果表明，此方法能有效地对心脏灌注磁共振图像进行配准处理。     

Rigid Registration of Medical Images Using 1D and 2D Binary Projections

Image registration is a necessary procedure in everyday clinical practice. Several techniques for rigid and non-rigid registration have been developed and tested and the state-of-the-art is evolving from the research setting to incorporate image registration techniques into clinically useful tools. In this paper, we develop a novel rigid medical image registration technique which incorporates binary projections. This technique is tested and compared to the standard mutual information (MI) methods. Results show that the method is significantly more accurate and robust compared to MI methods. The accuracy is well below 0.5° and 0.5 mm. This method introduces two more improvements over MI methods: (1)for 2D registration with the use of 1D binary projections, we use minimal interpolation; and (2) for 3D registration with the use of 2D binary projections the method converges to stable final positions, independent of the initial misregistration.     

A Nonrigid Registration of MR Breast Images Using Complex-valued Wavelet Transform

In this paper, a fast, slice-by-slice, nonrigid registration algorithm of dynamic magnetic resonance breast images is presented. The method is based on a multiresolution motion estimation of the breast using complex discrete wavelet transform (CDWT): the pyramid of oriented complex subimages is used to implement a hierarchical phase-matching-based motion estimation algorithm. The resulting motion estimate is nonrigid and pixel-independent. To assess the method performance, we computed the correlation coefficient and the normalized mutual information between pre- and postcontrast images with and without realignment. The indices increased after using our approach and the improvement was superior to rigid or affine registration. A set of clinical scores was also evaluated. The clinical validation demonstrated an increased readability in the subtraction images. In particular, CDWT registration allowed a best definition of breast and lesion borders and greater detail detectability.     

Joint edge detection and motion estimation of cardiac MR image sequence by a phase field method

In this paper a variational framework for joint segmentation and motion estimation is employed for inspecting heart in Cine MRI sequences. A functional including Mumford-Shah segmentation and optical flow based dense motion estimation is approximated using the phase-field technique. The minimizer of the functional provides an optimum motion field and edge set by considering both spatial and temporal discontinuities. Exploiting calculus of variation principles, multiple partial differential equations associated with the Euler-Lagrange equations of the functional are extracted, first. Next, the finite element method is used to discretize the resulting PDEs for numerical solution. Several simulation runs are used to test the convergence and the parameter sensitivity of the method. It is further applied to a comprehensive set of clinical data in order to compare with conventional cascade methods. Developmental constraints are identified as memory usage and computational complexities, which may be resolved utilizing sparse matrix manipulations and similar techniques. Based on the results of this study, joint segmentation and motion estimation outperforms previously reported cascade approaches especially in segmentation. Experimental results substantiated that the proposed method extracts the motion field and the edge set more precisely in comparison with conventional cascade approaches. This superior result is the consequence of simultaneously considering the discontinuity in both motion field and image space and including consequent frames (usually five) in our joint process functional.     

Fast Mutual Information Registration Method of 3-D Medical Image

INTRODUCTION　　Multimodal registration,which brings images from different modality into spa-tial correspondence,is of importance in many clinical applications.Over the years,research of multimodal registration has produced a lotof differentmethods.Surveysof medical image registration with a classification have been made〔1,2〕.The registra-tion methods can be classified as frame- based,point- landmark- based,surface- basedand voxel- based.Voxel- based methods achieve registration by iden…     

基于形态学梯度和互信息的医学图像配准方法

基于互信息的图像配准方法,已被广泛用于医学图像的配准.但是该方法计算量较大且无法处理图像空间信息,导致运行时间较长且易陷入局部极值.为解决此问题,本研究提出了一种基于形态学梯度和互信息相结合的医学图像配准新方法,该方法充分利用图像的灰度信息和空间几何形状,可节省运行时间且有效改善传统互信息方法中的局部极值问题.实验结果表明,该方法的配准精度和运行速度明显优于传统方法.     

基于伪超声和互信息的磁共振与超声图像配准

手术中超声图像与术前磁共振图像的配准在手术导航系统中非常重要。我们利用磁共振和超声成像原理,提出了基于伪超声和互信息,并结合多分辨率技术与Powell优化算法对两种模态图像进行配准的方法,该方法可以有效降低寻优过程中陷入局部极值收敛的概率,提高两种模态图像的配准精度。实验结果表明,我们提出的基于伪超声和互信息的配准方法比目前手术导航系统中普遍采用的标记点方法具有更高的配准精度。     

基于配准方法的颅脑CT图像病变信息自动提取算法

医学图像中病变信息的计算机自动提取是实现计算机智能辅助诊断的关键与难点,本研究的目的就是提出一个解决该难题的算法,称之为PATHOINFER。该算法的基本过程是首先选择一幅具有代表性的模板图像帆和一系列与其相应的正常图像样奉Mi,利用非刚性配准分别建立表示“正常图像”灰度变化的灰度均值图谱,表示正常变异的统计概率图谱和反映其解剖结构空间关系的分割模板。以实现对“正常图像”的计算机描述。再通过M0与目标图像S的配准,达到“正常图像”与S在空间关系上的一致,然后通过S与“正常图像”的比较,利用模糊逻辑推理,自动检出S中的病变区域,并实现对其病变特征信息的自动提取。实验结果表明,PATHOINFER算法可自动地检出并分割病变区域,并能够自动地提取包括病变发生部位在内的特征信息。实现了计算机智能辅助诊断研究中病变信息自动提取的难胚。     

Automatic Classification of Left Ventricular Regional Wall Motion Abnormalities in Echocardiography Images Using Nonrigid Image Registration

Identification and classification of left ventricular (LV) regional wall motion (RWM) abnormalities on echocardiograms has fundamental clinical importance for various cardiovascular disease assessments especially in ischemia. In clinical practice, this evaluation is still performed visually which is highly dependent on training and experience of the echocardiographers and therefore suffers from significant interobserver and intraobserver variability. This paper presents a new automatic technique, based on nonrigid image registration for classifying the RWM of LV in a three-point scale. In this algorithm, we register all images of one cycle of heart to a reference image (end-diastolic image) using a hierarchical parametric model. This model is based on an affine transformation for modeling the global LV motion and a B-spline free-form deformation transformation for modeling the local LV deformation. We consider image registration as a multiresolution optimization problem. Finally, a new regional quantitative index based on resultant parameters of the hierarchical transformation model is proposed for classifying RWM in a three-point scale. The results obtained by our method are quantitatively evaluated to those obtained by two experienced echocardiographers visually as gold standard on ten healthy volunteers and 14 patients (two apical views) and resulted in an absolute agreement of 83 % and a relative agreement of 99 %. Therefore, this diagnostic system can be used as a useful tool as well as reference visual assessment to classify RWM abnormalities in clinical evaluation.     

基于多目标交叉变异粒子群算法的多模医学图像非刚性配准

以互信息为相似性测度,采用B样条变换对多模态医学图像进行非刚性配准时,由于噪声及图像插值等原因造成的互信息局部极值使得传统优化方法不能搜索到最佳配准参数。为此,使用粒子群智能优化方法作为搜索策略,以降低对图像预处理的要求,进一步提高基于互信息的非刚性配准的鲁棒性。为了克服粒子群算法受初始值选取等因素的影响易陷于局部最优的缺点,使用LBFGS优化得到的结果构造初始粒子群,采用多目标优化方法结合交叉变异策略加以改进,使得算法在解空间搜索的遍历性得到改善,优化结果更接近全局最优。MR-T2与MR-PD图像的配准实验证明,该方法提高了基于互信息的B样条非刚性配准的鲁棒性,配准率达到94%;CT与PET图像的配准实验表明该方法相比惯性权重粒子群算法提高了配准精度,互信息增加了0.026;另外,CT与CBCT图像的配准实验也验证了本方法的有效性。     

Medical Image Registration Based on Phase Congruency and Regional Mutual Information

In this paper, a new approach of multimodality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. Firstly, instead of standard mutual information ( MI ) based on joint intensity histogram, regional mutual information ( RMI ) is employed, which allows neighborhood information to be taken into account. Secondly, a new feature images obtained by means of phase congruency are invariants to brightness or contrast changes. By incorporating these features and intensity into RMI, we can combine the aspects of both structural and neighborhood information together, which offers a more robust and a high level of registration accuracy.     

基于互信息医学刚性图像配准方法的研究

目的:基于互信息的配准方法是医学图像配准领域的重要方法,具有鲁棒性,精度高等优点。本文探究医学刚性图像配准的有效算法和关键技术。方法:基于互信息配准方法,利用Powell多参数算法和改进的PV插值算法,得到两幅图像之间的最大互信息和最佳配准参数。结果:二维磨牙CT图像配准实验表明,配准速度快,精度提高,验证了插值方法的有效性。结论:方法和算法可提高配准速度,能有效抑制互信息目标函数的局部极值。     

利用相关比相似性测度多分辨率配准MR和PET医学图像的方法

采用了全新的相关比相似性测度作为配准的测度准则，提出了有效的磁共振(MR)和正电子发射端层扫描(PET)临床医学图像配准方法。具体设计时，采用了加速的多分辨率的配准方案，对方案中涉及的几何变换选取、重采样、多分辨率体数据表达及最优化方法进行了详细的设计分析。最后，利用多分辨率配准方法，对MR和PET临床医学图像进行配准，给出了令人满意的效果，同时和基于体素灰度的直接配准法相比，配准速度也有了很大提高。     

NIRViz: 3D Visualization Software for Multimodality Optical Imaging Using Visualization Toolkit (VTK) and Insight Segmentation Toolkit (ITK)

Optical imaging using near-infrared light is used for noninvasive probing of tissues to recover vascular and molecular status of healthy and diseased tissues using hemoglobin contrast arising due to absorption of light. While multimodality optical techniques exist, visualization techniques in this area are limited. Addressing this issue, we present a simple framework for image overlay of optical and magnetic resonance (MRI) or computerized tomographic images which is intuitive and easily usable, called NIRViz. NIRViz is a multimodality software platform for the display and navigation of Digital Imaging and Communications in Medicine (DICOM) MRI datasets and 3D optical image solutions geared toward visualization and coregistration of optical contrast in diseased tissues such as cancer. We present the design decisions undertaken during the design of the software, the libraries used in the implementation, and other implementation details as well as preliminary results from the software package. Our implementation uses the Visualization Toolkit library to do most of the work, with a Qt graphical user interface for the front end. Challenges encountered include reslicing DICOM image data and coregistration of image space and mesh space. The resulting software provides a simple and customized platform to display surface and volume meshes with optical parameters such as hemoglobin concentration, overlay them on magnetic resonance images, allow the user to interactively change transparency of different image sets, rotate geometries, clip through the resulting datasets, obtain mesh and optical solution information, and successfully interact with both functional and structural medical image information.     

基于最大互信息的人脑多模图像快速配准算法

对脑图谱开发过程中来源于不同成像设备的多模图像进行配准。对预处理后的数码图像和MRI图像,首先提取图像的轮廓,采用基于轮廓的力矩主轴法计算初始平移量和旋转量,然后设定初始缩放系数,将此初始配准参数作为改进单纯形法的初始参数,以互信息作为相似性测度迭代搜索,使互信息最大,从而实现最佳配准。结果表明本算法不需要人为预调整待配准图像的分辨率,自动化程度高,配准速度快,精度较高,能够满足脑图谱开发过程中的多模图像配准要求。