人体CT切片图像中骨骼的分割

在实现人体骨骼的三维可视化中 ,首要的一步是将骨骼从二维图像中分割出来。本研究选用美国国家医学图书馆提供的可视人体项目中 1733张女性 CT切片数据 ,提出了一套图像去噪、分割和平滑的处理方法。在去噪中使用了 Chebyshev一致逼近滤波技术 ;在分割中提出了一种简单实用的自适应阈值法 ,将图像之间的相关性与区域生长法结合 ;后处理中使用形态学方法、多分辨率滤波等算法。完成了对所有 1733张图片的分割 ,经过与原图的对照 。

Pulmonary nodules: optimal slice thickness of CT in revealing bronchial imageology

Objective： The aim of our study was to determine an optimal slice thickness that was efficient in revealing bronchial imageology of pulmonary nodules （PNs） on multi-slice spiral computed tomography （MSCT） images preliminarily. Methods： Fifty-four patients with 62 PNs （diameter 〈 3 cm） underwent multidetector-row computed tomography of the chest in a single-breath-hold technique. The raw data were acquired with a collimation of 0.625 mm. Three sets of contiguous im- ages were reconstructed with 1-, 2-, and 5-mm slice thickness, respectively. Bronchial imageology of SPNs on the CT images presented in 1-, 2-, and 5-mm slice thickness was compared. Using the 1-mm sections as the gold standard, an optimal slice thickness in revealing bronchial imageology of PNs was determined. Results： Bronchial imageology of PNs on the CT images presented in 1 mm slice thickness involved 85 bronchi （35 second-fourth generation bronchi; 50 fifth-eighth generation bronchi）. Bronchial imageology on 2-mm-thick sections was as same as that on I-mm-thick sections in 34 second- fourth generation bronchi. No statistically significant difference in number of second- fourth generation bronchi with same bronchial imageology was found between that on 2-mm-thick images and I-mm-thick images （P = 0.836 〉 0.05）. Bronchial imageology on 5-mm-thick sections was as same as that on 1-mm-thick sections in 24 second-fourth generation bronchi. There was statistically significant difference in number of second-fourth generation bronchi with same bronchial imageology between that on 5-mm-thick images and 1-mm-thick images （P = 0.026 〈 0.05）. Bronchial imageology on 2-mm-thick sections was as same as that on 1-mm-thick sections in 38 fifth-eighth generation bronchi. There was statistically significant difference in number of fifth-eighth generation bronchi with same bronchial imageology between that on 2-mm-thick images and 1-mm- thick images （P = 0.029 〈 0.05）. Bronchial imageology on 5-mm-thick images was as same as that on I-mm-thick images in 31 fifth-eighth generation bronchi. There was statistically significant difference in number of fifth-eighth generation bronchi with same bronchial imageology between that on 5-mm-thick sections and I-mm-thick sections （P = 0.001 〈 0.05）. Conclusion： It is suggested that the use of 2-mm slice thickness is suitable in revealing second- fourth generation bronchi and the use of l-mm slice thickness is suitable in revealing fifth-eighth generation bronchi.

基于体素标号法的空间球粒体分布测量

提出一种基于切片图像的体素标号测量算法。通过对CT图像序列作预处理建立点云模型,由外向内对体素进行序数标号,采用局部极值搜索和体心精简得到球粒体体心,由体心标号值完成统计。实验结果表明,随机点噪声范围在35%以内时,算法能准确统计出存在粘连的球粒体的数目与空间分布,且体积估算误差小于5%。

基于超分辨率改进Faster R-CNN的点阵结构内部缺陷判识方法

利用增材制造技术制备金属三维点阵结构件过程中,结构内部经常会出黏连、断裂等多种细小缺陷,导致样件结构功能下降。根据缺陷结构与正常结构之间的特征区别,提出了一种针对金属点阵结构内部出现的细小缺陷自动判识的方法。利用X-射线微聚焦CT扫描金属点阵结构获得原始输入图片,在Faster R-CNN(Faster region-based convolutional neural networks)框架的基础上,改进原有特征提取网络,开发图像超分辨率重建模块。通过对工业CT图片的局部细节特征增强,实现了快速有效地识别细小缺陷的类型,以及缺陷位置信息的标注。试验证明,改进Faster R-CNN模型对金属点阵结构样件内部的两种典型细小缺陷识别的平均正确率高达93.5%。研究结果表明,通过超分辨率网络对图像进行放大,可以提高细小缺陷的特征提取,通过加深网络加强特征学习,从而实现了点阵结构内部细小缺陷的自动判识。