大鼠松质骨切片图像的三维重建与定量分析

本文研究动物松质骨连续切片图像数据集的获取、分割、配准、及三维重建的技术方法.利用病理切片和图像数码摄入技术,获取了大鼠腰椎松质骨连续切片图像数据集,用基于外置标记点和分割-计数法两种方法进行参数计算,依参数对图像进行刚体变换完成配准,将配准后的切片图像输入二维图像处理软件进行分割,提取感兴趣区域后输入三维重建软件进行三维重建与定量分析.重建后的松质骨三维立体图像呈均匀、致密的立体网状结构,骨小梁连接清晰可见.     

虚拟中国人女性一号松质骨图像数据的配准与三维重建

目的：研究从虚拟人体数据集中松质骨连续切片图像的分割、配准、及三维重建的技术方法。方法：利用现有的虚拟中国人女性一号数据集中腰椎和股骨部分解剖连续切片数据集，用基于外置标记点和分割—计数法两种方法进行参数计算，依参数对图像进行刚体变换完成配准，将配准后的切片图像输入二维图像处理软件进行分割，提取感兴趣区域后输入三维重建软件进行三维重建。结果：重建后的松质骨三维立体图像呈均匀、致密的立体网状结构，骨小梁连接清晰可见。结论：利用现有软件及技术可重建虚拟人体的精细结构。     

基于图谱的肝脏CT三维自动分割研究

目的在肝脏外科手术或肝脏病理研究中,计算肝脏体积是重要步骤。由于肝脏外形复杂、临近组织灰度值与之接近等特点,肝脏的自动医学图像分割仍是医学图像处理中的难点之一。方法本文采用图谱结合3D非刚性配准的方法,同时加入肝脏区域搜索算法,实现了鲁棒性较高的肝脏自动分割程序。首先,利用20套训练图像创建图谱,然后程序自动搜索肝脏区域,最后将图谱与待分割CT图像依次进行仿射配准和B样条配准。配准以后的图谱肝脏轮廓即可表示为目标肝脏分割轮廓,进而计算出肝脏体积。结果评估结果显示,上述方法在肝脏体积误差方面表现出色,达到77分,但在局部（主要在肝脏尖端）出现较大的误差。结论该方法分割临床肝脏CT图像具有可行性。     

利用图谱匹配分割标注VHP数据集

利用TT脑图谱中丰富的结构信息,本文提出了一种自动分割脑图像的方法,并将其用于Visible Human数据集（VHD）的脑图像的分割,这种方法可分为两步,首先,将VHD中的脑图像和TT Atlas配准,通过图像和医学图谱的匹配,可以把图谱中存储的拓朴信息直接映射到ＶＨＤ,然后,利用这个预分割的模板对VHD脑图像进行模糊聚类分割,为自动将模板中的结构信息用于分割,本文利用Chamfer距离变换,提出了一中引入形状因子的FCM聚类算法。     

基于ITK多图谱配准的脑部皮质下核团的分割研究

大量研究表明,阿尔茨海默症(AD)的病变与大脑皮质下核团的萎缩息息相关,某些核团的萎缩(如海马)可能成为AD疾病早期诊断的标志,而皮质下核团的分割是研究核团萎缩模式的重要前提。基于AD患者和正常人各30例3DT1W-MR图像,先结合直方图分析和三维形态学分析方法对图像进行脑组织提取,后采用ITK配准算法将10个脑图谱图像经两阶段分别配准到提取脑组织后的图像空间。第一阶段实现基于均方差的仿射配准,第二阶段实现基于互信息的B样条形变配准,两阶段的配准均采用线性插值法和梯度下降的优化搜索方法。最后采用STAPLE融合算法,对配准后得到的10个目标图像进行图像融合,得到最终的分割结果。结果表明:除尾状核外,分割得到的其余6对核团的体积与常用的FSL-FIRST算法的分割结果无统计学差别(P>0.05);AD患者的右侧伏核和双侧海马发生萎缩(P<0.05)。因此,基于ITK配准框架的多图谱配准分割方法能有效分割MR图像上边界不明确的皮质下核团。     

结合置信连接度的自适应模糊连接度的MRI图像中丘脑分割算法研究

目的立体定向神经外科手术中,丘脑及其子结构神经核团作为靶区,常被用于治疗癫痫和锥体外系疾病。利用计算机对丘脑神经核团进行分割,对于神经外科疾病的诊断与治疗具有重要的研究价值。为提高丘脑神经核团的分割精度,简化人工操作,减少人工干预,避免主观影响,本文提出一种结合置信连接度的自适应模糊连接度混合算法,用于分割丘脑结构。方法本文算法在基于模糊连接度的框架内增加图像梯度特征,采用自适应权重及自动选取感兴趣区域的方式,对10例人脑MRI图像数据的丘脑结构进行分割。结果实验结果与专家指导下的手工分割结果进行比较,并对两者之间的相似度进行量化比较。结果表明该算法在减少人工干预的同时保证了较高的准确率。结论结合置信连接度的自适应模糊连接度丘脑及其子结构分割算法在计算速度和精度上均优于传统模糊连接度算法。     

基于灰度和形状的非刚性图像配准算法的研究

提出一种新的灰度和形状信息相结合的全自动同模态医学图像非刚性配准-分割算法,将欧氏距离表示的形状信息融入基于灰度的配准算法中,构造出新的代价函数.该算法在医学图像多目标分割的应用中,能够较好地完成灰度相近、边缘模糊、间距较小的不同结构的分割.对5组真实脑部MRI图像进行分割脑深层灰质结构的实验,结果表明,本算法优于基于灰度信息的图像配准算法.     

结合脑图谱和水平集的MR图像分割的研究

本文利用脑图谱的先验知识并结合水平集等算法实现对脑MR图像的初步分割。主要步骤：（1）选取数字脑图谱，对图谱进行预处理；（2）实现图谱与脑MR图像的配准；（3）利用图谱提供的轮廓信息对水平集算法进行初始化，完成颅骨和脑脊液的提取以及脑白质和脑灰质的分割。实验结果表明，利用脑图谱提供的信息可有效解决水平集算法初始化问题，缩小求解空间，减少迭代次数，该方法具有较好的鲁棒性。     

生物组织连续切片图像的配准与三维显示

连续组织切片图像的的三维重建和显示，是一种重要的形态学研究方法，三维重建过程中，首先要对连续切片图像进行配准，本文首先介绍了作者提出的用于自动图像配准的分割一计数法，该方法通过对图像做简单的阈值分割，将优化的准则函数定义为图像的联合直方图特定区域上的计数值，大大加快了配准速度，然后将该方法用于小鼠胚胎的连续切片图像配准，得到空间上配准的三维数据场，为三维显示奠定了基础，最后给了一个初步的表面绘制结果。     

基于血管匹配的三维超声与CT图像的配准方法

提出一种基于血管匹配的三维超声与CT图像配准的新方法.首先,基于水平集方法自动分割出CT图像中的血管；其次,由于超声图像中的声影与血管均属于低回声区域,我们结合声影形成的物理原理及图像纹理特性,自动检测出声影区域,以提高配准的鲁棒性；最后,采用进化算法,将CT图像中分割出的血管与超声图像中低回声区域进行匹配.在肝脏体模和临床脾脏数据上进行了实验验证,自动配准的成功率在95％以上,平均目标配准误差在2 mm以内,实验结果验证了本方法的可行性.     

Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information

An automatic method for delineating the prostate (including the seminal vesicles) in three-dimensional magnetic resonance scans is presented. The method is based on nonrigid registration of a set of prelabeled atlas images. Each atlas image is nonrigidly registered with the target patient image. Subsequently, the deformed atlas label images are fused to yield a single segmentation of the patient image. The proposed method is evaluated on 50 clinical scans, which were manually segmented by three experts. The Dice similarity coefficient (DSC) is used to quantify the overlap between the automatic and manual segmentations. We investigate the impact of several factors on the performance of the segmentation method. For the registration, two similarity measures are compared: Mutual information and a localized version of mutual information. The latter turns out to be superior (median DeltaDSC approximately equal 0.02, p < 0.01 with a paired two-sided Wilcoxon test) and comes at no added computational cost, thanks to the use of a novel stochastic optimization scheme. For the atlas fusion step we consider a majority voting rule and the "simultaneous truth and performance level estimation" algorithm, both with and without a preceding atlas selection stage. The differences between the various fusion methods appear to be small and mostly not statistically significant (p > 0.05). To assess the influence of the atlas composition, two atlas sets are compared. The first set consists of 38 scans of healthy volunteers. The second set is constructed by a leave-one-out approach using the 50 clinical scans that are used for evaluation. The second atlas set gives substantially better performance (DeltaDSC=0.04, p < 0.01), stressing the importance of a careful atlas definition. With the best settings, a median DSC of around 0.85 is achieved, which is close to the median interobserver DSC of 0.87. The segmentation quality is especially good at the prostate-rectum interface, where the segmentation error remains below 1 mm in 50% of the cases and below 1.5 mm in 75% of the cases.     

Adaptation of a 3D prostate cancer atlas for transrectal ultrasound guided target-specific biopsy

Due to lack of imaging modalities to identify prostate cancer in vivo, current TRUS guided prostate biopsies are taken randomly. Consequently, many important cancers are missed during initial biopsies. The purpose of this study was to determine the potential clinical utility of a high-speed registration algorithm for a 3D prostate cancer atlas. This 3D prostate cancer atlas provides voxel-level likelihood of cancer and optimized biopsy locations on a template space (Zhan et al 2007). The atlas was constructed from 158 expert annotated, 3D reconstructed radical prostatectomy specimens outlined for cancers (Shen et al 2004). For successful clinical implementation, the prostate atlas needs to be registered to each patient's TRUS image with high registration accuracy in a time-efficient manner. This is implemented in a two-step procedure, the segmentation of the prostate gland from a patient's TRUS image followed by the registration of the prostate atlas. We have developed a fast registration algorithm suitable for clinical applications of this prostate cancer atlas. The registration algorithm was implemented on a graphical processing unit (GPU) to meet the critical processing speed requirements for atlas guided biopsy. A color overlay of the atlas superposed on the TRUS image was presented to help pick statistically likely regions known to harbor cancer. We validated our fast registration algorithm using computer simulations of two optimized 7- and 12-core biopsy protocols to maximize the overall detection rate. Using a GPU, patient's TRUS image segmentation and atlas registration took less than 12 s. The prostate cancer atlas guided 7- and 12-core biopsy protocols had cancer detection rates of 84.81% and 89.87% respectively when validated on the same set of data. Whereas the sextant biopsy approach without the utility of 3D cancer atlas detected only 70.5% of the cancers using the same histology data. We estimate 10-20% increase in prostate cancer detection rates when TRUS guided biopsies are assisted by the 3D prostate cancer atlas compared to the current standard of care. The fast registration algorithm we have developed can easily be adapted for clinical applications for the improved diagnosis of prostate cancer.     

基于点信息的人体寰椎三维模型配准

目的建立一种基于点信息的寰椎三维模型局部点配准方法,为进行三维数据的统计建模奠定基础。方法以正常人体CT序列图像生成寰椎三维模型30个,所有模型标注人工选择的对应点,设为模板模型1个,训练模型20个,验证模型9个。首先进行训练组模型对模板模型的配准,包括点信息的比较计算和权重系数的机器训练两步,以自动配准点与人工选点的欧式距离之和为测度,获得点配准公式及对应的最佳系数;其次以验证组模型对模板模型进行配准,统计自动配准点与人工选点的欧式距离值,同训练组做对比分析,评估方法的稳定性。结果获得配准函数及对应的最佳权重系数,训练组和验证组配准结果误差分别为1.983和2.045 mm,统计分析表明两组结果没有显著性差异。结论此方法精度及稳定性均达到预期目的,可用于寰椎模型之间感兴趣点的自动配准及统计建模工作中的元素分类。     

MARS: a mouse atlas registration system based on a planar x-ray projector and an optical camera

This paper introduces a mouse atlas registration system (MARS), composed of a stationary top-view x-ray projector and a side-view optical camera, coupled to a mouse atlas registration algorithm. This system uses the x-ray and optical images to guide a fully automatic co-registration of a mouse atlas with each subject, in order to provide anatomical reference for small animal molecular imaging systems such as positron emission tomography (PET). To facilitate the registration, a statistical atlas that accounts for inter-subject anatomical variations was constructed based on 83 organ-labeled mouse micro-computed tomography (CT) images. The statistical shape model and conditional Gaussian model techniques were used to register the atlas with the x-ray image and optical photo. The accuracy of the atlas registration was evaluated by comparing the registered atlas with the organ-labeled micro-CT images of the test subjects. The results showed excellent registration accuracy of the whole-body region, and good accuracy for the brain, liver, heart, lungs and kidneys. In its implementation, the MARS was integrated with a preclinical PET scanner to deliver combined PET/MARS imaging, and to facilitate atlas-assisted analysis of the preclinical PET images.     

Gland and Zonal Segmentation of Prostate on T2W MR Images

For many years, prostate segmentation on MR images concerned only the extraction of the entire gland. Currently, in the focal treatment era, there is a continuously increasing need for the separation of the different parts of the organ. In this paper, we propose an automatic segmentation method based on the use of T2W images and atlas images to segment the prostate and to isolate the peripheral and transition zones. The algorithm consists of two stages. First, the target image is registered with each zonal atlas image then the segmentation is obtained by the application of an evidential C-Means clustering. The method was evaluated on a representative and multi-centric image base and yielded mean Dice accuracy values of 0.81, 0.70, and 0.62 for the prostate, the transition zone, and peripheral zone, respectively.     

灰度与形状同步匹配的非刚性配准研究

基于灰度的非刚性配准算法一般假设参考图像和浮动图像对应结构之间的灰度保持一致,然而在基于图谱的图像配准应用中,这种假设往往不符合实际。本文在给出一种可以同时校正灰度和形状差异的弹性配准算法的同时,针对该算法不能校正局部微小形变的弱点,提出采用自由项变换的方法进行校正以提高配准精度。配准实验基于20个IBSR真实脑部MRI图像,结果表明配准后图像与参考图像间的互相关系数得到明显提高。实验证明,本文提出的方法不仅能够同时校正形状差异和灰度变化,而且具有较高的配准质量。