基于形状相似性和曲线化简的统计形状模型建立

针对活动形状模型(ASM)中统计模型建立存在的标记点的自动选取和点的对应两大问题,提出了一个完整的解决方案:首先通过矩阵的相似变换对齐训练集中各样本,然后根据样本特征,选择基准样本,再利用曲线化简方法在基准样本上自动提取特征点,最后利用训练样本间形状的相似性,由基准样本上的特征点向其它样本投影得到所有样本卜的标记点,实现了标记点的自动选取.同时保证了样本间标记点的一一对应.为了验证该方法的精确性和有效性.分别建立股骨和髋臼骨的点分布模型,实验结果表明,模型形状变化表现力强、计算效率高.分别与传统的手工标记法和优化法比较,在具有相同形状表现力的情况下,本文方法建模效率更高,时间开销更小.     

Fluoroscopic bone fragment tracking for surgical navigation in femur fracture reduction by incorporating optical tracking of hip joint rotation center

A new method for fluoroscopic tracking of a proximal bone fragment in femoral fracture reduction is presented. The proposed method combines 2-D and 3-D image registration from single-view fluoroscopy with tracking of the head center position of the proximal femoral fragment to improve the accuracy of fluoroscopic registration without the need for repeated manual adjustment of the C-arm as required in stereo-view registrations. Kinematic knowledge of the hip joint, which has a positional correspondence with the femoral head center and the pelvis acetabular center, allows the position of the femoral fragment to be determined from pelvis tracking. The stability of the proposed method with respect to fluoroscopic image noise and the desired continuity of the fracture reduction operation is demonstrated, and the accuracy of tracking is shown to be superior to that achievable by single-view image registration, particularly in depth translation.     

Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT

We present a gradient-based method for rigid registration of a patient preoperative computed tomography (CT) to its intraoperative situation with a few fluoroscopic X-ray images obtained with a tracked C-arm. The method is noninvasive, anatomy-based, requires simple user interaction, and includes validation. It is generic and easily customizable for a variety of routine clinical uses in orthopaedic surgery. Gradient-based registration consists of three steps: 1) initial pose estimation; 2) coarse geometry-based registration on bone contours, and; 3) fine gradient projection registration (GPR) on edge pixels. It optimizes speed, accuracy, and robustness. Its novelty resides in using volume gradients to eliminate outliers and foreign objects in the fluoroscopic X-ray images, in speeding up computation, and in achieving higher accuracy. It overcomes the drawbacks of intensity-based methods, which are slow and have a limited convergence range, and of geometry-based methods, which depend on the image segmentation quality. Our simulated, in vitro, and cadaver experiments on a human pelvis CT, dry vertebra, dry femur, fresh lamb hip, and human pelvis under realistic conditions show a mean 0.5-1.7 mm (0.5-2.6 mm maximum) target registration accuracy.     

Unsupervised segmentation and quantification of anatomical knee features: data from the Osteoarthritis Initiative

This paper presents a fully automated method for segmenting articular knee cartilage and bone from in vivo 3-D dual echo steady state images. The magnetic resonance imaging (MRI) datasets were obtained from the Osteoarthritis Initiative (OAI) pilot study and include longitudinal images from controls and subjects with knee osteoarthritis (OA) scanned twice at each visit (baseline, 24 month). Initially, human experts segmented six MRI series. Five of the six resultant sets served as reference atlases for a multiatlas segmentation algorithm. The methodology created precise knee segmentations that were used to extract articular cartilage volume, surface area, and thickness as well as subchondral bone plate curvature. Comparison to manual segmentation showed Dice similarity coefficient (DSC) of 0.88 and 0.84 for the femoral and tibial cartilage. In OA subjects, thickness measurements showed test-retest precision ranging from 0.014 mm (0.6%) at the femur to 0.038 mm (1.6%) at the femoral trochlea. In the same population, the curvature test-retest precision ranged from 0.0005 mm(-1) (3.6%) at the femur to 0.0026 mm(-1) (11.7%) at the medial tibia. Thickness longitudinal changes showed OA Pearson correlation coefficient of 0.94 for the femur. In conclusion, the fully automated segmentation methodology produces reproducible cartilage volume, thickness, and shape measurements valuable for the study of OA progression.     

A new approach to the laboratory evaluation of the essential parameters of revision hip arthroplasty

It would be great if the parts used in a traditional hip replacement lasted forever, but they don't. The surgery performed when the parts need to be repaired is called revision hip arthroplasty. The best method of revision is decided by one of three situations: injury, change in the joint components, or severe loss of bone stock. Orthopedic surgeons have observed that soon after revision surgery, when the patient is walking, the joint may become loose from displacement of the acetabulum, or it becomes too stiff or nonweight bearing. The implant may experience unsuccessful results because of rejection or poor rebuilding of tissues. There is an increasing number of unsuccessful operations, and the possible causes are being investigated in many research centers. The causes may be material defects, excessive wear of polyethylene elements, loosening of the artificial acetabulum, or wrong surgical techniques. Considering the significant cost of revision hip arthroplasty, long-lasting functionality of the prosthesis would lead to considerable savings.     

Fluoroscopy-based 3-D reconstruction of femoral bone cement: a new approach for revision total hip replacement

In revision total hip replacement the removal of the distal femoral bone cement can be a time consuming and risky operation due to the difficulty in determining the three-dimensional (3-D) boundary of the cement. We present a new approach to reconstruct the bone cement volume by using just a small number of calibrated multiplanar X-ray images. The modular system design allows the surgeon to react intraoperatively to problems arising during the individual situation. When encountering problems during conventional cement removal, the system can be used on demand to acquire a few calibrated X-ray images. After a semi-automatic segmentation and 3-D reconstruction of the cement with a deformable model, the system guides the surgeon through a free-hand navigated or robot-assisted cement removal. The experimental evaluation using plastic test implants cemented into anatomic specimen of human femoral bone has shown the potential of this method with a maximal error of 1.2 mm (0.5 mm RMS) for the distal cement based on just 4-5 multiplanar X-ray images. A first test of the complete system, comparing the 3-D-reconstruction with a computed tompgraphy data set, confirmed these results with a mean error about 1 mm.     

Fluid Vector Flow and Applications in Brain Tumor Segmentation

In this paper, we propose a new approach that we call the ldquofluid vector flowrdquo (FVF) active contour model to address problems of insufficient capture range and poor convergence for concavities. With the ability to capture a large range and extract concave shapes, FVF demonstrates improvements over techniques like gradient vector flow, boundary vector flow, and magnetostatic active contour on three sets of experiments: synthetic images, pediatric head MRI images, and brain tumor MRI images from the Internet brain segmentation repository.     

A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data

We developed a highly automated three-dimensionally based method for the segmentation of bone in volumetric computed tomography (CT) datasets. The multistep approach starts with three-dimensional (3-D) region-growing using local adaptive thresholds followed by procedures to correct for remaining boundary discontinuities and a subsequent anatomically oriented boundary adjustment using local values of cortical bone density. We describe the details of our approach and show applications in the proximal femur, the knee, and the skull. The accuracy of the determination of geometrical parameters was analyzed using CT scans of the semi-anthropomorphic European spine phantom. Depending on the settings of the segmentation parameters cortical thickness could be determined with an accuracy corresponding to the side length of 1 to 2.5 voxels. The impact of noise on the segmentation was investigated by artificially adding noise to the CT data. An increase in noise by factors of two and five changed cortical thickness corresponding to the side length of one voxel. Intraoperator and interoperator precision was analyzed by repeated analysis of nine pelvic CT scans. Precision errors were smaller than 1% for trabecular and total volumes and smaller than 2% for cortical thickness. Intraoperator and interoperator precision errors were not significantly different. Our segmentation approach shows: 1) high accuracy and precision and is 2) robust to noise, 3) insensitive to user-defined thresholds, 4) highly automated and fast, and 5) easy to initialize.     

An estimation/correction algorithm for detecting bone edges in CT images

The normal direction of the bone contour in computed tomography (CT) images provides important anatomical information and can guide segmentation algorithms. Since various bones in CT images have different sizes, and the intensity values of bone pixels are generally nonuniform and noisy, estimation of the normal direction using a single scale is not reliable. We propose a multiscale approach to estimate the normal direction of bone edges. The reliability of the estimation is calculated from the estimated results and, after re-scaling, the reliability is used to further correct the normal direction. The optimal scale at each point is obtained while estimating the normal direction; this scale is then used in a simple edge detector. Our experimental results have shown that use of this estimated/corrected normal direction improves the segmentation quality by decreasing the number of unexpected edges and discontinuities (gaps) of real contours. The corrected normal direction could also be used in postprocessing to delete false edges. Our segmentation algorithm is automatic, and its performance is evaluated on CT images of the human pelvis, leg, and wrist.     

基于序列间先验约束和多视角信息融合的肝脏CT图像分割

医学电子计算机断层扫描(CT)序列图像中肝脏的准确分割是实现计算机辅助肝手术的重要前提,然而图像中存在的组织病变、边界模糊或缺失、不同组织间的粘连给肝脏分割带来极大挑战。针对这些问题,该文提出一种基于图像序列间先验约束的半自动分割方法,并进一步采取了多视角信息融合的方式实现肝脏的准确分割。该方法的优势在于无需大量数据的收集和复杂的先验训练。在Sliver07公开数据集合的验证结果显示,和领域内主要方法相比,该方法具有较高的分割准确度,特别是当肝脏区域存在病灶、边界模糊或缺失的情况下具有明显提升。     

An Integrated Segmentation and Analysis Approach for QCT of the Knee to Determine Subchondral Bone Mineral Density and Texture

We have developed a new integrated approach for quantitative computed tomography of the knee in order to quantify bone mineral density (BMD) and subchondral bone structure. The present framework consists of image acquisition and reconstruction, 3-D segmentation, determination of anatomic coordinate systems, and reproducible positioning of analysis volumes of interest (VOI). Novel segmentation algorithms were developed to identify growth plates of the tibia and femur and the joint space with high reproducibility. Five different VOIs with varying distance to the articular surface are defined in the epiphysis. Each VOI is further subdivided into a medial and a lateral part. In each VOI, BMD is determined. In addition, a texture analysis is performed on a high-resolution computed tomography (CT) reconstruction of the same CT scan in order to quantify subchondral bone structure. Local and global homogeneity, as well as local and global anisotropy were measured in all VOIs. Overall short-term precision of the technique was evaluated using double measurements of 20 osteoarthritic cadaveric human knees. Precision errors for volume were about 2-3% in the femur and 3-5% in the tibia. Precision errors for BMD were about 1-2% lower. Homogeneity parameters showed precision errors up to about 2% and anisotropy parameters up to about 4%.     

Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT

This paper proposes a robust statistical estimation and verification framework for characterizing the ellipsoidal (anisotropic) geometrical structure of pulmonary nodules in the Multislice X-ray computed tomography (CT) images. Given a marker indicating a rough location of a target, the proposed solution estimates the target's center location, ellipsoidal boundary approximation, volume, maximum/average diameters, and isotropy by robustly and efficiently fitting an anisotropic Gaussian intensity model. We propose a novel multiscale joint segmentation and model fitting solution which extends the robust mean shift-based analysis to the linear scale-space theory. The design is motivated for enhancing the robustness against margin-truncation induced by neighboring structures, data with large deviations from the chosen model, and marker location variability. A chi-square-based statistical verification and analytical volumetric measurement solutions are also proposed to complement this estimation framework. Experiments with synthetic one-dimensional and two-dimensional data clearly demonstrate the advantage of our solution in comparison with the gamma-normalized Laplacian approach (Linderberg, 1998) and the standard sample estimation approach (Matei, 2001). A quasi-real-time three-dimensional nodule characterization system is developed using this framework and validated with two clinical data sets of thin-section chest CT images. Our experiments with 1310 nodules resulted in (1) robustness against intraoperator and interoperator variability due to varying marker locations, (2) 81% correct estimation rate, (3) 3% false acceptance and 5% false rejection rates, and (4) correct characterization of clinically significant nonsolid ground-glass opacity nodules. This system processes each 33-voxel volume-of-interest by an average of 2 s with a 2.4-GHz Intel CPU. Our solution is generic and can be applied for the analysis of blob-like structures in various other applications.     

Constrained detection of left ventricular boundaries from cine CT images of human hearts

Detection of the left ventricular (LV) endocardial (inner) and epicardial (outer) boundaries in cardiac images, provided by fast computer tomography (cine CT), magnetic resonance (MR), or ultrasound (echocardiography), is addressed. The automatic detection of the LV boundaries is difficult due to background noise, poor contrast, and often unclear differentiation of the tissue characteristics of the ventricles, papillary muscles, and surrounding tissues. An approach to the automatic ventricular boundary detection that employs set-theoretic techniques, and is based on incorporating a priori knowledge of the heart geometry, its brightness, spatial structure, and temporal dynamics into the boundaries detection algorithm is presented. Available knowledge is interpreted as constraint sets in the functional space, and the consistent boundaries are considered to belong to the intersection of all the introduced sets, thus satisfying the a priori information. An algorithm is also suggested for the simultaneous detection of the endocardial and epicardial boundaries of the LV. The procedure is demonstrated using cine CT images of the human heart.     

Segmentation of cancellous bone from high-resolution computed tomography images: influence on trabecular bone measurements

The quantification of cancellous bone network from computed tomography (CT) images requires a segmentation step which is crucial and difficult because of the partial volume effect in CT images. In this paper, we present and evaluate a new approach for segmenting cancellous bone network from high-resolution CT (HRCT) slices. The idea is first to detect a skeleton from the crest lines of the structure and then to thicken it to extract the whole bone structure by satisfying local neighborhood constraints. The segmentation requires the adjustment of relative and not absolute parameters like most methods. We quantified the influence of these parameters on architectural measurements. Results were first validated by using a physical phantom and then examined on a series of 12 HRCT images of human lumbar vertebra of different ages. We demonstrated that the choice of segmentation parameters yielded important variability on architectural measurements (up to 20%), but less variability than a more commonly used approach. This stresses the importance of settle on the segmentation parameters for once, which is possible with the proposed method.     

Voxel similarity measures for 3-D serial MR brain image registration

The authors have evaluated eight different similarity measures used for rigid body registration of serial magnetic resonance (MR) brain scans. To assess their accuracy the authors used 33 clinical three-dimensional (3-D) serial MR images, with deformable extradural tissue excluded by manual segmentation and simulated 3-D MR images with added intensity distortion. For each measure the authors determined the consistency of registration transformations for both sets of segmented and unsegmented data. They have shown that of the eight measures tested, the ones based on joint entropy produced the best consistency. In particular, these measures seemed to be least sensitive to the presence of extradural tissue. For these data the difference in accuracy of these joint entropy measures, with or without brain segmentation, was within the threshold of visually detectable change in the difference images     

视频对象分割与两种面向对象的视频编码器

在基于对象的视频编码中,视频对象的分割是重要的任务.本文研究一种利用位移帧差(DFD)的高阶统计特性和数学形态学算子的视频对象自动分割方法.这种方法首先根据一组转移帧差的高阶矩来得到一个大体覆盖运动对象的区域(模板),接着用形态学的腐蚀算子从模板的边沿向内腐蚀,直到对象的边沿.提出一种简单而高效的基于查找人头肩像轮廓最大转折点的头肩分离算法.在分割的基础上,用软件实现了一种基于MPEG-4的视频编码系统.提出一种面向对象分配带宽(OOBA——object-oriented bandwidth allocation)的极低比特率视频编码器.该编码器与传统基于帧的视频编码器相比,在低比特率环境下,PSNR略为下降,但图像的主观视觉质量得到提高.     

Trabecular Bone Analysis in CT and X-Ray Images of the Proximal Femur for the Assessment of Local Bone Quality

Currently, conventional X-ray and CT images as well as invasive methods performed during the surgical intervention are used to judge the local quality of a fractured proximal femur. However, these approaches are either dependent on the surgeon's experience or cannot assist diagnostic and planning tasks preoperatively. Therefore, in this work a method for the individual analysis of local bone quality in the proximal femur based on model-based analysis of CT- and X-ray images of femur specimen will be proposed. A combined representation of shape and spatial intensity distribution of an object and different statistical approaches for dimensionality reduction are used to create a statistical appearance model in order to assess the local bone quality in CT and X-ray images. The developed algorithms are tested and evaluated on 28 femur specimen. It will be shown that the tools and algorithms presented herein are highly adequate to automatically and objectively predict bone mineral density values as well as a biomechanical parameter of the bone that can be measured intraoperatively.     

兔股骨近端骨性结构的解剖研究

目的研究兔股骨近端解剖结构及股骨头钻孔减压细胞注入的可行性。方法以20只新西兰兔为实验动物,对殴骨近端进行Micro—CT、大体测量,观察兔股骨近端骨性解剖特点。结果兔股骨近端转子窝较深,股骨颈最小直径〉4mm。结论以2mm克氏针及骨髓穿刺针可有效完成钻孔减压及细胞注入.     

Wavelet transform filtering and nonlinear anisotropic diffusion assessed for signal reconstruction performance on multidimensional biomedical data

Computer tomography (CT) techniques are the most widely applicable noninvasive methods for obtaining two- and three-dimensional insights into biological objects. They comprise CT for medical applications, as well as electron tomography used for investigating macromolecular and cellular specimens. Recent advances in the recording schemes improve the speed and resolution frontiers and provide new insights into structural organizations of different objects. However, many data sets suffer from a poor signal-to-noise ratio, which severely hinders the application of methods for automated data analysis, such as feature extraction, segmentation, and visualization. We propose the multidimensional implementation of two powerful signal reconstruction techniques, namely invariant wavelet filtering and nonlinear anisotropic diffusion. We establish quantitative measures to assess the signal reconstruction performance on synthetic data and biomedical images. The appropriate multidimensional implementations of wavelet and diffusion techniques allow for a superior performance over conventional noise-reduction methods. We derive the conditions for the choice between wavelet and diffusion techniques with respect to an optimal signal reconstruction performance. Results of applying the proposed methods in two very different imaging domains-molecular biology and clinical research-are provided.     

基于改进C-V模型的肾脏CT图像分割方法

生物组织的自动分割是计算机辅助诊断和病变 检测的关键步骤。在腹腔CT图像中,肾脏组织本身的灰度不均匀性使得传统C-T模型无法准 确实现肾脏的分割。为了解决上述问题,本文结合图像全局和 局部统计信息改进了传统的C-V模型。 基于先验知识,提出了描述肾脏组织皮质 特征的数学表达式。选择感兴趣区域,在预处理阶段获得了CT图像中肾脏的大致初始轮廓。 随后,应用C-V模型进行轮廓演化时引入局域信息,提高了C-V模型的局部适应性。实验结 果表明,与现有方法相比,本文的方法的结果更接近于人工分割结果,其肾脏分割结果的Di ce系数平均值为94.0%。