基于多相位图像相似性的肺4D-CT超分辨率重建

肺4D-CT为放射治疗提供全面的图像引导,在当今肺癌治疗中起着重要的作用。然而,由于采集时间和人体所能承受辐射剂量的限制,无法得到高分辨率（[Z]轴方向）CT图像,因此,通常采集到的肺4D-CT图像[Z]轴方向细节部分往往不够清晰。针对这一问题,提出了一个基于多相位相似性的非局部均值超分辨率重建方法,来提升肺4D-CT图像的质量。该方法利用多相位图像之间的互补信息,以非局部均值滤波为基础,来恢复图像的高分辨率细节结构;另外,由于呼吸运动,肺4D-CT图像中不同相位图像肺区域的灰度会有所差异,为保证重建高分辨率图像的灰度不变性,引入全局约束以修正重建图像的整体灰度。采用一套公共肺4D-CT数据集评估提出的方法,该数据集由10组肺4D-CT数据组成,每组数据包含10个相位。实验表明,在恢复图像的细节和增强分辨率方面,该方法要优于传统的线性插值和凸集投影（Projection Onto Convex Set,POCS）超分辨率重建算法。     

基于自适应薄板样条全变分的肺CT/PET图像配准

全变分正则项虽然能够在具有滑移运动的肺等胸腹部器官图像配准时校正边界不连续位移场，但仍然无法保留图像的局部特征，损失配准精度。针对肺图像CT单模配准和CT/PET双模配准，通过像素点空间位置权重将薄板样条能量算子与全变分算子进行空间加权建立自适应薄板样条全变分正则项。然后，将正则项与CRMI相似性测度以及L-BFGS优化方法结合建立非刚性配准算法。通过DIR-Lab 4D-CT公共数据集和CT/PET临床数据集上的实验结果表明，提出的方法能够在保证边界不连续运动的同时保证图像内部的平滑性，具有更高的配准精度。     

频率域基于梯度预处理的互相关图像配准方法

传统的频率域图像配准方法有两种：基于互相关的图像配准方法和基于相位相关的图像配准方法。这两种方法都是通过确定逆傅立叶变换域最大峰值位置来获得配准信息。互相关图像配准方法与相位相关图像配准方法相比,主要存在两个缺点：一是峰值的跨度过大,二是有时存在多个峰值。本文对传统方法进行了改进,提出了频率域基于梯度预处理互相关的图像配准方法,该方法首先对待配准图像进行梯度预处理,然后对预处理后的图像用传统的互相关方法进行配准。实验表明,该方法很好地克服了上述传统方法的不足,并能获得精确的配准结果。     

光纤耦合的亚像元图像超分辨率重建

光纤耦合图像之间存在亚像元级位移问题,为此,提出一种基于图像互补信息的超分辨率重建算法。采用传统相位相关法进行整像元级配准,通过拟合多个谱峰值进行亚像元级配准,利用待插值点周围的4条B样条曲线实现图像插值融合。实验结果表明,该算法能提高图像空间分辨率,且复杂度较低、重建图像质量较好。     

SAR图像的改进相位相关配准方法

基于相位相关的图像配准方法以其运算量小、抗干扰性强等优点得到广泛关注,但它只能解决具有平移运动的图像配准问题,为此,文章提出一种改进的相位相关配准方法,分析了其配准原理,给出流程图,并将其应用于存在缩放、旋转和平移变换的SAR图像配准中。仿真结果表明,该算法能够较为准确地估计出待配准图像相对于基准图像的平移参数、缩放尺度和旋转角度。     

全相位内插交错采样超分辨率融合

利用超分辨率重建技术可提高图像的空间分辨率,但是低分辨率图像序列的配准精度直接影响超分辨率重建的效果。如果能在现有硬件设备的基础上,通过合理安排CCD线阵对图像进行错位,就能对图像间的子像素位移进行精确控制,消除运动参数估计对图像超分辨率重建的制约,从而提高图像的空间分辨率。对超模式影像所代表的交错采样图像的超分辨率重建进行了研究,基于全相位滤波理论,构造了具有交错采样结构的全相位内插滤波器,并将其用于交错采样图像超分辨率内插融合。为交错采样图像超分辨率重建提供了快速、实用且性能优异的高分辨率图像初始估计,大大提高了图像超分辨率重建质量。     

基于加速稳健特征匹配和凸集投影算法的超分辨率重建

针对不理想的配准结果会导致超分辨率重建失败的问题,提出了一种基于加速健壮特征(SURF)匹配和凸集投影(POCS)的超分辨率重建算法。该算法首先采用SURF算法进行连续帧图像的配准,估计图像序列的运动位移;然后根据运动估计结果,在POCS理论框架下进行图像重建。实验结果表明,该方法能够较明显地改善图像的视觉效果,获得较丰富的细节信息,且具有较好的噪声抑制能力。     

基于一种优化的配准算法的图像超分辨率重建

提出了一种优化的图像配准算法.该算法充分结合了时域基于全局运动模型的配准算法和频域基于傅立叶变换的配准算法,并运用到超分辨重建中.假设序列低分辨率图像之间存在旋转和平移,首先运用全局配准法求出旋转参数,对图像进行旋转补偿,然后对补偿后的图像用频域配准法估计平移参数,针对估计出的旋转参数和平移参数对序列低分辨率图像进行超分辨率图像重建,实验表明该方法能较有效地提高图像分辨的效果,并且本文还阐述了低分辨率图像帧数的影响.     

基于肺部不连续运动的全变差正则化动态配准研究

胸部放射治疗计划的一个先决条件就是精确建模胸部器官的呼吸运动。虽然4D成像技术的出现使得呼吸过程中肺部能够可视化,但获得不同呼吸阶段体素的精确对应依然是一个充满挑战的难题。本文主要研究肺部放射治疗中的弹性配准问题,采用基于全变差(Total Variation,TV)正则化的快速自由形变(Fast Free-form Deformable,FFD)模型弹性配准方法处理胸部器官运动的不连续性。我们将配准问题建模为求解一个包含图像相似性测度与光滑性测度的能量泛函的最小值,通过变分法,将该能量泛函极小化问题转化为求解对应的Euler-Largange偏微分方程,利用有限差分法、三线性插值、牛顿迭代法,迭代求解出偏移场。我们在胸部二维CT影像、肺部三维CT影像、腹部三维MRI影像上对所提算法进行了验证,结果表明该算法在处理器官不连续性运动的配准方面比传统的最小二乘化优化算法以及基于2范数正则化的算法更具有优越性。该算法结合了TV norm保持图像边缘的能力以及FFD自由度高这两个优点,精度高、速度快、且全自动。随着现代4D放射治疗的影响日益增大,该方法能够在以后的临床中发挥作用。     

三维医学图像配准在图像引导放疗中的应用

为了确定病人的摆位误差,实现精确放疗,提出一种改进的Demons弹性配准算法。采用FDK算法对锥形束CT(CBCT)图像进行三维重建,利用可视化工具包 (VTK)体绘制法可视化重建结果;在分割与配准工具包 (ITK)基础上实现Demons算法,并基于对称梯度的思想,将参考图像和浮动图像的梯度场信息加入到Demons算法中,给出新的Demons形变力公式。分别使用单模态和多模态医学图像进行配准实验,结果显示改进的Demons算法与原始Demons算法相比,配准速度更快、精度更高。基于对称梯度的Demons算法更适用于图像引导放射治疗中CBCT重建图像与CT计划图像间的配准。     

A method for the registration of multiview range images acquired in forest areas using a terrestrial laser scanner

Terrestrial laser scanning can provide high-resolution, three-dimensional (3D) surveys of forest canopy surfaces. Applications that aim to build 3D forest scenes or to make plot-level or canopy-level assessments may ultimately require co-registration of range images from multiple viewpoints. Numerous challenges are reported in the co-registration of range images. A method for the registration of multiview range images acquired in forest areas using an inclinometer together with the positions of tree trunks and ground surface information is proposed in this study. Two of the six transformation parameters needed for co-registration were measured by an inclinometer. Another three transformation parameters were determined using the central positions of corresponding trunks in the two range images being registered. The last transformation parameter was determined by the fitness of ground surfaces in the two range images. A vertical filtering method is proposed for the extraction of trunk points. Similar triangles composed of trunks in the two range images were used to find corresponding trunks. Since the overlap of range images acquired at a station and that between stations was different, the registration process was divided into two stages. The range images acquired by prone scanners were first matched with the one acquired by a standing scanner at the same station. Then the range images acquired by standing scanners were used for the registration between stations. The results in our test site showed that an average registration accuracy of 0.74 cm with standard deviation (SD) of 0.23 cm within each station and an average accuracy of 3.0 cm with SD of 1.5 cm between stations were achieved.     

基于旋转多视角深度配准的三维重建方法

针对在旋转平台上采集得到的多视角数据,提出一种有效的配准方法,同时结合双目立体视觉测量构建了完整的三维重建系统。通过拍摄旋转平台上多个视角下的标定板图像,提取标定板图像角点信息,计算出旋转平台坐标系和摄像机坐标系的空间位置关系,进一步推导出不同视角下的坐标转换关系,从而实现不同视角的数据配准。实验结果表明,基于本配准方法的旋转多视角双目测量系统具有较高的配准精度,能有效用于物体表面三维重建。     

Estimating myocardial motion by 4D image warping

A method for spatio-temporally smooth and consistent estimation of cardiac motion from MR cine sequences is proposed. Myocardial motion is estimated within a four-dimensional (4D) registration framework, in which all three-dimensional (3D) images obtained at different cardiac phases are simultaneously registered. This facilitates spatio-temporally consistent estimation of motion as opposed to other registration-based algorithms which estimate the motion by sequentially registering one frame to another. To facilitate image matching, an attribute vector (AV) is constructed for each point in the image, and is intended to serve as a “morphological signature” of that point. The AV includes intensity, boundary, and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points to refine the registration. Experimental results on real data demonstrate good performance of the proposed method for cardiac image registration and motion estimation. The motion estimation is validated via comparisons with motion estimates obtained from MR images with myocardial tagging.     

基于改进Demons算法的三维肺部医学影像配准研究*

为实现相同个体在不同呼吸状态下产生较大形变的三维肺部医学影像配准,提出一种基于改进Demons算法的精确有效配准方案。首先,对待配准影像进行全局非刚性配准。通过尺度不变特征变算法对影像进行特征点提取与匹配,根据匹配结果计算变换参数,完成全局配准;其次,利用改进Demons算法对全局配准后的影像进行局部非刚性配准。使用改进的方案实现了人体肺部影像配准,并且肺部整体轮廓以及内部组织的配准结果较理想。配准前,影像间的均方误差值为25835.3,经配准后影像间均方误差值降为3726.31,均方误差值下降率为85.58%。提出的方案能够有效配准三维肺部影像,为对肺部呼吸运动估计以及呼吸功能分析提供良好的基础。     

基于肺部组织特征的图像弹性配准研究

图像配准是一种建立两幅图像空间对应关系的过程,它被广泛应用于计算机视觉、遥感数据分析及图像处理中,特别是在影像引导放射治疗领域,图像配准发挥着巨大作用。但由于受呼吸运动的影响,精确的肺部影像配准依然是一个充满挑战的难题。目前,尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)已被用于医学图像配准中,并且取得了较理想的结果。然而,SIFT检测到的仅是图像的块特征,不能有效的反映肺部的运动。文章提出了一种基于Harris和SIFT算子的杂交型特征检测方法,这种方法能有效检测肺部的组织特征,如血管分叉点和肺部边界等。除此之外,为了有效去除特征匹配过程中产生的错配点,还提出了一种基于互相关和组织结构不变性的滤除错配点方法。文章最后采用一系列不同呼吸周期的肺部CT影像来对所提出的算法进行验证。定性和定量的结果表明,该算法较传统的SIFT算法更具优越性。     

Hand-held 3D scanning based on coarse and fine registration of multiple range images

A hand-held 3D scanning technique is proposed to reconstruct 3D models of real objects. A sequence of range images captured from a hand-held stereo camera is automatically registered to a reference coordinate system. The automated scanning process consists of two states, coarse and fine registration. At the beginning, scanning process starts at the fine registration state. A fast and accurate registration refinement technique is used to align range images in a pair-wise manner. If the refinement technique fails, the process changes to the coarse registration state. A feature-based coarse registration technique is proposed to find correspondences between the last successful frame and the current frame. If the coarse registration successes, the process returns to the fine registration state again. A fast point-to-plane refinement technique is employed to do shape-based registration. After the shape-based alignment, a texture-based refinement technique matches texture features to enhance visual appearance of the reconstructed models. Through a graphic and video display, a human operator adjusts the pose of the camera to change the view of the next acquisition. Experimental results show that 3D models of real objects are reconstructed from sequences of range images.     

肺结节计算机辅助检测与定位系统*

为了实现肺结节的智能识别,开发了肺结节计算机辅助检测与定位系统（SCADP）。该系统包括肺实质分割、候选肺结节分割、肺结节特征选择与提取、肺结节分类、图像配准与融合、结节三维重建与定位和勾画病灶等功能模块。其中,采用活动轮廓模型的分割方法实现候选肺结节分割,采用基于规则与专家系统的决策方法实现肺结节分类;采用自由变形法实现图像配准。基于小波变换的融合方法,以区域标准差与区域能量相结合的融合规则实现多模图像的融合;基于改进的Shear-Warp算法快速实现体绘制。实验证明,该肺结节计算机辅助检测与定位系统满足肺结节计算机辅助诊断要求。     

Fingerprint image mosaicking by recursive ridge mapping.

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model. The proposed method also effectively eliminates erroneous correspondences and decides how well the transformation is estimated by calculating the registration error with a normalized distance map. The proposed method consists of three phases: feature extraction, transform estimation, and mosaicking. Transform is initially estimated with matched minutia and the ridges attached to them. Unpaired ridges in the overlapping area between two images are iteratively matched by minimizing the registration error, which consists of the ridge matching error and the inverse consistency error. During the estimation, erroneous correspondences are eliminated by considering the geometric relationship between the correspondences and checking if the registration error is minimized or not. In our experiments, the proposed method was compared with three existing methods in terms of registration accuracy, image quality, minutia extraction rate, processing time, reject to fuse rate, and verification performance. The average registration error of the proposed method was less than three pixels, and the maximum error was not more than seven pixels. In a verification test, the equal error rate was reduced from 10% to 2.7% when five images were combined by our proposed method. The proposed method was superior to other compared methods in terms of registration accuracy, image quality, minutia extraction rate, and verification.     

虚拟结肠镜中皱襞预匹配的结肠配准

目的 虚拟结肠镜是一种采用CT或者MRI图像重建出结肠3维结构,通过漫游虚拟结肠来检测结肠组织,一般用于早期结直肠癌筛查。结肠配准能够有效提高息肉检测的效率和精确度,但由于仰卧和俯卧位下的结肠图像形变太大,现有的配准方案中特征点的提取没有考虑到较多特殊情况,因此需要寻找一个新的配准方案完成完整的结肠配准。方法 提出了一种新的结肠图像配准方法,能够完成不同体位获取的虚拟结肠图像之间的配准。首先提取可以反映结肠结构信息的皱襞特征,用模板匹配和特征匹配方法找出两幅结肠中匹配的皱襞对。然后将匹配对的中心点作为标记点,做基于标记点的非刚性粗配准,最后将两幅图做B样条配准完成细配准。这种方法能够将结肠内部较大的形变先矫正,使得两幅图之间的形变缩小到一定范围,然后利用传统配准方法能够完成配准。结果 在5套数据中,找到能够成功匹配的皱襞区域数量占所有分割出的皱襞区域总数量的62%左右,匹配错误率为4.7%左右。完成皱襞粗配准后,结肠形变明显趋于一致,灰度值相对误差减小,最终完成了结肠配准。结论 先进行皱襞匹配再做基于匹配好的皱襞的映射关系做结肠配准,能够将存在较大形变的两套结肠匹配起来。在之后的工作中需要量化特征点选取对配准结果的影响,同时在做配准评估时,单纯采用灰度差值不能很好完成评估,因为灰度特征只能一定程度反映整体差异,不能很好体现结构差异,需要添加其他评估标准辅助配准评估。