旋转扫描超声心脏图像的三维直接匹配插值

针对一组相互相交且空间呈一定角度排列的非并行二维心脏图像,提出了不同于传统插值方法的（对体元的）三维直接匹配插值法。插值时,首先获取各二维断层图像,确定各断层图像内“空体元”的三维空间位置;其次构造立方柱体,通过“空体元”的匹配像素块在柱体与最近邻原始图像相交的上下窗口内寻找运动轨迹;最后基于匹配块对“空点”进行插值。该方法将传统层间插值方法的两次插值过程简化为一次插值过程,从而获取系列的断层图像     

基于非刚体配准的多分辨率B样条层间插值方法

三维图像的处理和操作需要将一般的断层序列插值成为具有各坐标轴一致的分辨率的体数据。而目前最常用的线性插值方法在层间距较大时会导致图像边缘模糊和出现伪影。Penney根据现有的非刚体匹配方法,提出了利用图像形变场数据的插值算法,明显提高了层间插值的质量。本研究对Penney提出的算法进行了改进,用多分辨率B样条拟合插值方法替代Penney使用的最邻近直线插值方法,并将新算法的实验结果与原算法、线性插值进行了对比。新算法既保持了高质量插值又提高了计算速度,并且使生成的图像达到了C^2平滑。此算法可用于精度要求比较高的体数据插值重建过程。     

基于对应点匹配的断层图像三维插值方法

用于放疗计划的ＣＴ和ＭＲＩ图像断导蝗距离通常大于断层内部像素间的距离,三维剂量场的计算等工作通常需要等间隔分布的三维图像数据。目前常用的断层间直接线性值的方法分引起图像边界模糊,针对这个问题,本文提出了一种基于相邻两个断层图像对应点匹配的插值新方法,首先在相邻断层间建立起点与点的对应关系,然后再利用这些对应点插值出新的图像数据。模拟结果表明,客中方法能有效地消除直接线性插值带来的图像边界模糊的问题     

基于非刚体配准的体数据插值重建方法

三维图像的处理和操作需要将一般的断层序列插值成为具有各坐标轴一致的分辨率的体数据，而目前最常用的线性插值方法在层间距较大时会导致图像边缘模糊和出现伪影。Penney根据现有的非刚体匹配方法，提出了利用图像形变场数据的插值算法，大大提高了层间插值的质量。本文对Penney提出的算法进行了两方面的改进，在配准过程中用简单的单射性约束取代了复杂的平滑性约束，用邻域平均算法替代Penney使用的最邻近直线插值方法，并将新算法的实验结果与原算法、线性插值进行了对比。新算法在保持高质量插值的前提下提高了计算速度。该算法可以应用于精度要求比较高的体数据插值重建过程。     

基于局部平面信息的三维灰度图像方向内插方法

灰度图像插值是体绘制的预处理步骤,插值结果的好坏,直接影响着绘制的效果。通过,插值错误主要出现的组织的边界处,组织的表面上灰度的变化较小,如果在表面上做插值,就可以保证插值在相同组织之间进行,避免了常用的线性插值会产生新组织的错误,而在组织内部,由于灰度值变化不大,只要做简单的线性插值就可以了,基于上述考虑,本文以局部平面近似组织的表面,将局部表面方向和梯度信息用于三维灰度图像的内插重建,充分利用     

一种快速有效的医学图像灰度插值算法

介绍一种用于快速有效的医学图像灰度插值算法。通过在两幅已知图像上寻找对应点，有效地解决了线性插值边界模糊的问题，同时避免了基于形状的插值算法中图像分割的问题，具有较强的适用范围。实验结果表明此算法能得到令人满意的插值结果。     

基于分层策略的凝胶图像间蛋白点匹配算法

凝胶图像间蛋白点比对分析是蛋白质组学研究的重要方法,针对凝胶图像间蛋白点人工比对工作量大和易出错等问题,提出一种基于分层策略的凝胶图像间蛋白点自动匹配算法。首先将每张凝胶图像中的蛋白点按不同灰度等级划分到对应层级中;然后根据蛋白点的灰度相关性,采用归一化互相关法对各层级蛋白点进行粗匹配;最后以粗匹配蛋白点点对作为标记特征点,采用几何相关法和相似性准则,实现蛋白点间的精确匹配。通过对国际凝胶图和Bio-Rad公司测试图等不同图源的凝胶图像进行蛋白点匹配实验,结果表明,该算法具有较高的匹配精度,其匹配误差小于4%,对不同来源和不同图像形变的凝胶图像都具有较好的稳定性。     

旋转扫描超声心脏图像的中轴匹配与图像插值

研究旋转扫描超声医学图像多维重建中图像中轴匹配方法和插值算法。首先 ,根据图像中轴的特征点分析其时域曲线 ,实现中轴的轴向匹配 ;相似矩阵用来寻找两幅图像中轴附近匹配的像素线。自相关函数和 Fouri-er谱用来对图像中轴的匹配进行评价。其次 ,针对旋转扫描原始图像空间分布的特殊性 ,提出了分区插值法。实验结果表明 :我们提出的图像中轴匹配方法切实可行 ,插值算法快速、精确 ,基于匹配的插值算法可大大改善重建质量     

一种螺旋CT的非线性插值算法

螺旋CT有多种重建的插值算法 ,主要分为 36 0°插值和 180°插值两类 ,通常采用线性插值函数为插值核。现提出一种基于B -样条函数的非线性插值算法 ,可以在提高Z轴分辨率的同时 ,改善信噪比 ,提高图像分辨率。     

基于SIFT特征和近似最近邻算法的医学CT图像检索

针对医学X线计算机断层(Computed Tomography,CT)图像,提出了一种基于尺度不变特征变换(Scale InvariantFeature Transform,SIFT)特征和近似最近邻算法的检索方法。首先通过SIFT算法得到图像的特征点和相应的特征向量,再采用近似最近邻算法进行SIFT特征向量的匹配搜索,得到数据库中与参考图像最相似的图像序列。实验结果表明,该法能检索到与目标图像细节相符的结果,大大提高了检索速度。与传统的基于纹理的检索方法相比,查准率和检索结果与目标图像的相似程度方面更佳,符合医学CT图像检索的要求。     

Cone beam volume CT image artifacts caused by defective cells in x-ray flat panel imagers and the artifact removal using a wavelet-analysis-based algorithm

The application of x-ray flat panel imagers (FPIs) in cone beam volume CT (CBVCT) has attracted increasing attention. However, due to a deficient semiconductor array manufacturing process, defective cells unavoidably exist in x-ray FPIs. These defective cells cause their corresponding image pixels in a projection image to behave abnormally in signal gray level, and result in severe streak and ring artifacts in a CBVCT image reconstructed from the projection images. Since a three-dimensional (3-D) back-projection is involved in CBVCT, the formation of the streak and ring artifacts is different from that in the two-dimensional (2-D) fan beam CT. In this paper, a geometric analysis of the abnormality propagation in the 3D back-projection is presented, and the morphology of the streak and ring artifacts caused by the abnormality propagation is investigated through both computer simulation and phantom studies. In order to calibrate those artifacts, a 2D wavelet-analysis-based statistical approach to correct the abnormal pixels is proposed. The approach consists of three steps: (1) the location-invariant defective cells in an x-ray FPI are recognized by applying 2-D wavelet analysis on flat-field images, and a comprehensive defective cell template is acquired; (2) based upon the template, the abnormal signal gray level of the projection image pixels corresponding to the location-invariant defective cells is replaced with the interpolation of that of their normal neighbor pixels; (3) that corresponding to the isolated location-variant defective cells are corrected using a narrow-windowed median filter. The CBVCT images of a CT low-contrast phantom are employed to evaluate this proposed approach, showing that the streak and ring artifacts can be reliably eliminated. The novelty and merit of the approach are the incorporation of the wavelet analysis whose intrinsic multi-resolution analysis and localizability make the recognition algorithm robust under variable x-ray exposure levels between 30% and 70% of the dynamic range of an x-ray FPI.     

Shape-based grey-level image interpolation.

The three-dimensional (3D) object data obtained from a CT scanner usually have unequal sampling frequencies in the x-, y- and z-directions. Generally, the 3D data are first interpolated between slices to obtain isotropic resolution, reconstructed, then operated on using object extraction and display algorithms. The traditional grey-level interpolation introduces a layer of intermediate substance and is not suitable for objects that are very different from the opposite background. The shape-based interpolation method transfers a pixel location to a parameter related to the object shape and the interpolation is performed on that parameter. This process is able to achieve a better interpolation but its application is limited to binary images only. In this paper, we present an improved shape-based interpolation method for grey-level images. The new method uses a polygon to approximate the object shape and performs the interpolation using polygon vertices as references. The binary images representing the shape of the object were first generated via image segmentation on the source images. The target object binary image was then created using regular shape-based interpolation. The polygon enclosing the object for each slice can be generated from the shape of that slice. We determined the relative location in the source slices of each pixel inside the target polygon using the vertices of a polygon as the reference. The target slice grey-level was interpolated from the corresponding source image pixels. The image quality of this interpolation method is better and the mean squared difference is smaller than with traditional grey-level interpolation.     

Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach

Automatic segmentation of tumors is a complicated and difficult process as most tumors are rarely clearly delineated from healthy tissues. A new method for probabilistic segmentation to efficiently segment tumors within CT data and to improve the use of digital medical data in diagnosis has been developed. Image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels' spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices. The seed selection algorithm performs a greedy search by searching for pixels with matching intensity close to the location of the original seed point. A total of ten CT datasets were used as test cases, each with varying difficulty in terms of automatic segmentation. Five test cases had mean false positive error rates less than 10%, and four test cases had mean false negative error rates less than 10% when compared to manual segmentation of those tumors by radiologists.     

Polygon interpolation for serial cross sections

In this paper, a new technique for contour interpolation between slices is presented. We assumed that contour interpolation is equivalent to the interpolation of a polygon that approximates the object shape. The location of each polygon vertex is characterized by a set of parameters. Polygon interpolation can be performed on these parameters. These interpolated parameters are then used to reconstruct the vertices of the new polygon. Finally, the contour is approximated from this polygon using a cubic spline interpolation. This new technique takes into account the shape, the translation, the size, and the orientation of the object's contours. A comparison with regular shape-based interpolation is made on several object contours. The preliminary results show that this new method yields a better contour and is computationally more efficient than shape-based interpolation. This technique can be applied to gray-level images too. The interpolation result of an MR image does not show artifact of intermediate substance commonly seen in a typical linear gray-level interpolation.     

Artifacts associated with implementation of the Grangeat formula

To compensate for image artifacts introduced in approximate cone-beam reconstruction, exact cone-beam reconstruction algorithms are being developed for medical x-ray CT. Although the exact cone-beam approach is theoretically error-free, it is subject to image artifacts due to the discrete nature of numerical implementation. We report a study on image artifacts associated with the Grangeat algorithm as applied to a circular scanning locus. Three types of artifacts are found, which are thorn, wrinkle, and V-shaped artifacts. The thorn pattern is created by inappropriate extrapolation into the shadow zone in the radon domain. If the shadow zone is filled in with continuous data, the thorn artifacts along the boundary of the shadow zone can be removed. The wrinkle appearance arises if interpolated first derivatives of the radon data are not smooth between adjacent detector planes. In particular, the nearest-neighbor interpolation method should not be used. If the number of projections is not small, the bilinear interpolation method is effective to suppress the wrinkle artifacts. The V-shaped artifacts on the meridian plane come from the line integrations through the transition zones where derivative data change abruptly. Two remedies are to increase the sampling rate and suppress data noise.     

基于改进离散曲线演化模型的牙齿三维特征点提取方法研究

在牙种植技术中,牙齿特征点的有效提取对后续的三维配准和重建具有重要的意义,现有方法的计算效率比较低;论文改进了离散曲线演化算法,采用曲线特征因子量描述牙齿CT各断层图像边缘曲线的复杂性,并根据曲线特征因子量自适应确定不同层间图像边缘曲线特征点提取的数目,以降低数据存储的冗余量,提高特征点的提取效率;用改进的离散曲线演化算法对牙齿不同层的临床CT图像提取特征点,并将实验结果与现有离散曲线演化算法的结果进行比较。结果表明,改进方法在提取每层CT图像特征点所需时间约为原算法的50%,同时提取的特征点数约为原算法的80%。将改进方法提取的特征点按不同比率进行三次样条曲线插值并进行后期重建,其重建效果能很好地反映牙齿的真实结构。因此,改进方法的计算效率远高于离散曲线演化算法,在牙齿种植领域中具有临床应用前景。