基于磁定位器的手动三维超声图像标定

与二维超声相比，三维超声图像可以提供更多更详细的位置信息，有利于定位精度的提高。在基于磁定位器的手动三维超声成像中，可以直接获得各幅图像所对应的磁定位接收器位置和姿态，仍然需要做进一步的标定才能得到各幅图像在世界坐标系的位置。标定目标是获得磁定位接收器与超声图像之间确定的位置姿态关系。为此，设计制作了一种N形线模板，将其斜置于水槽中，从不同的位置和角度扫描该模板，每幅图像上将有3个亮点，以“N”斜线上的点作为特征点，采用最小二乘法进行标定计算，获得了磁定位接受器与超声图像之间的位姿关系。实验证明该标定方法简单易行，标定精度满足超声引导的需求，为进一步三维重构奠定了基础。     

胎儿颜面部三维超声基准标准切面自动校对系统的研究

提出三维超声胎儿颜面部三正交基准标准切面自动校对系统。首先,通过自动检测胎儿双眼球定位正中矢状面;其次,通过自动检测正中矢状面上的面部轮廓线定位面额冠状切面;最后,以鼻骨、双眼球和面部轮廓线为参考校对水平横切面,从而自动定义胎儿颜面部三维基准向量。对80个三维超声体数据的实验结果表明,我们的自动校对系统与超声医生校对的正中矢状面、面额冠状切面和水平横切面三个基准标准切面的角度误差（degree）分别为：3．836±2．954、4．870±3．822、4．805±4．005,距离误差（毫米）分别为2．022±1．707、2．921±1．887、2．543±1．578。实验结果验证了本系统是可行的、可重复的和可靠的。     

组织多普勒超声心脏图像的动态三维重建

传统的超声心脏图像三维重建技术只限于描述三维及动态三维解剖结构,不能对心脏动态功能做出准确有效的评价。本研究将建立组织多普勒超声心脏图像的动态三维重建方法,通过超声医学图像三维重建技术和组织多普勒超声成像技术的结合,重建心脏运动的动态三维加速度场,为心脏功能的准确评价提供一条新的途径。论文解决了加速度矢量场重建过程中关键的矢量插值和融合显像的问题,从组织多普勒加速度图像中分别重建心肌运动三维加速度场和三维解剖结构,并进行融合显像。试验结果证明了二者之间相对空间位置正确,该方法可以为心脏疾病的诊断和心脏功能的评价提供更多信息,在心脏靶点起搏和心内消融等领域有潜在的应用价值。     

基于最大熵的超声图像中胎儿股骨分割方法的研究

提出一种基于最大熵分割的胎儿股骨自动测量方法.首先,用中值滤波器对原始图像进行去噪,并用最大熵分割方法对去噪后的图像进行分割,得到股骨候选区域;其次,利用股骨区域位置、形状等特征信息对股骨候选区域进行筛选,得到最终的股骨区域;最后,通过股骨区域的外接矩形斜边长,计算股骨长度;与医生手动测量结果对比,70幅超声图像的自动测量结果平均相对误差为1.42±4.48 mm,实验结果验证了本方法的可行性.     

基于简比的C臂图像标记点自动匹配方法

针对C形臂X光机图像校正与标定过程中存在大量标记点需要自动对应问题,提出一种基于简比的标记点图像坐标与世界坐标自动对应方法.首先在白行设计的双层校准模板的底层模板上制作3个大的标记点,以在校准模板及其C臂图像上建立仿射坐标系;其次通过建方平面单应关系,将利用形态学方法提取出的校准模板的标记点图像坐标转换为仿射坐标;然后利用仿射变换共线三点简比不变性质,将图像仿射坐标归整,实现与校准模板标记点的自动对应;最后利用标记点投影性质区分上下两层标记点,并消除因投影失真产生的错误对应,从而实现标记点图像坐标与空间点坐标的自动准确对应.大量的测试试验表明,该方法稳定性好、鲁棒性强,具有实现简单、无需人工介入等优点,可应用于基于C臂X光图像的手术导航系统中.     

基于快速匹配算法的颈动脉超声图像的运动分析

颈动脉超声图像中的运动信息能够间接地反应颈动脉弹性等状况,结合颈动脉内中膜厚度(CIMT)能够为心脑血管疾病诊断提供定性与定量的依据.我们将改进后的金字塔快速匹配算法(modified block sum pyramid,MBSP)应用于颈动脉超声波图像斑点跟踪获得运动信息.理论和实验结果都表明,改进后的金字塔块匹配算法能有效地减少运动跟踪的运算量,并且有着和改进前的金字塔块匹配算法相同的准确度.运动跟踪结果能够为医生诊断心脑血管疾病起到一定的辅助作用.     

Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net

Automatic computerized segmentation of fetal head from ultrasound images and head circumference (HC) biometric measurement is still challenging, due to the inherent characteristics of fetal ultrasound images at different semesters of pregnancy. In this paper, we proposed a new deep learning method for automatic fetal ultrasound image segmentation and HC biometry: deeply supervised attention-gated (DAG) V-Net, which incorporated the attention mechanism and deep supervision strategy into V-Net models. In addition, multi-scale loss function was introduced for deep supervision. The training set of the HC18 Challenge was expanded with data augmentation to train the DAG V-Net deep learning models. The trained models were used to automatically segment fetal head from two-dimensional ultrasound images, followed by morphological processing, edge detection, and ellipse fitting. The fitted ellipses were then used for HC biometric measurement. The proposed DAG V-Net method was evaluated on the testing set of HC18 (n = 355), in terms of four performance indices: Dice similarity coefficient (DSC), Hausdorff distance (HD), HC difference (DF), and HC absolute difference (ADF). Experimental results showed that DAG V-Net had a DSC of 97.93%, a DF of 0.09 ± 2.45 mm, an AD of 1.77 ± 1.69 mm, and an HD of 1.29 ± 0.79 mm. The proposed DAG V-Net method ranks fifth among the participants in the HC18 Challenge. By incorporating the attention mechanism and deep supervision, the proposed method yielded better segmentation performance than conventional U-Net and V-Net methods. Compared with published state-of-the-art methods, the proposed DAG V-Net had better or comparable segmentation performance. The proposed DAG V-Net may be used as a new method for fetal ultrasound image segmentation and HC biometry. The code of DAG V-Net will be made available publicly on https://github.com/xiaojinmao-code/.     

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF) = 0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF = 0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI) = 2.     

Template Guided Live Wire and Its Application on Automatic Extraction of Tongue in Digital Image

In this paper, we propose a novel automatic object extraction algorithm, named the Template Guided Live Wire, based on the popularly used livewire techniques. We discuss in details the novel method＇s applications on tongue extraction in digital images. With the guides of a given template curve which approximates the tongue＇ s shape, our method can finish the extraction of tongue without any human intervention. In the paper, we also discussed in details how the template guides the live wire, and why our method functions more effectively than other boundary based segmentation methods especially the snake algorithm. Experimental results on some tongue images are as well provided to show our method＇s better accuracy and robustness than the snake algorithm.     

Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification is often hindered by the difficulty in distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided detection (CAD) scheme for the detection of lacunar infarcts. Although our previous CAD method indicated a sensitivity of 96.8 % with 0.71 false positives (FPs) per slice, further reduction of FPs remained an issue for the clinical application. Thus, the purpose of this study is to improve our CAD scheme by using template matching in the eigenspace. Conventional template matching is useful for the reduction of FPs, but it has the following two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the cross-correlation coefficient with these templates is time consuming. To solve these problems, we used template matching in the lower dimension space made by a principal component analysis. Our database comprised 1,143 T₁- and T₂-weighted images obtained from 132 patients. The proposed method was evaluated by using twofold cross-validation. By using this method, 34.1 % of FPs was eliminated compared with our previous method. The final performance indicated that the sensitivity of the detection of lacunar infarcts was 96.8 % with 0.47 FPs per slice. Therefore, the modified CAD scheme could improve FP rate without a significant reduction in the true positive rate.