CT辅助消化道胶囊内窥镜图像虚拟外翻

CT辅助消化道胶囊内窥镜图像方法的主要目的在于更为直观地提高胶囊内窥镜的小病灶检出率,减少胶囊内窥镜的重复检查次数,减少患者检查费用,提高医生的工作效率.现有的虚拟外翻技术主要有两种,一种是展平法,一种是镜像法.CT辅助消化道胶囊内窥镜虚拟外翻是以镜像法为指导思想对三维肠道模型进行外翻,实验结果证明了这一方法的有效性.三维图像的显示采用了经典的移动立方体的算法.验证完成后,算法通过TI DaVinciDSP异构双核处理器(DM6467)和接口进行加速实现.通过合理分配双核的任务,算法的运算效率得到了很大提高.     

无纸化考试系统自动组卷策略分析及改进

介绍了自动组卷的数学模型和几种常用的自动组卷策略,探讨了基于传统遗传算法的智能组卷策略,并在此基础上,重点研究了改进的型遗传算法组卷策略.     

基于非下采样Contourlets的CT／MRI图像自适应融合

结合人类视觉特性,针对CT／MRI医学图像的特点,提出了一种基于非下采样Contourlet变换的图像融合算法。先对源图像作非下采样Contourlet变换,完成图像的多尺度分析和方向分析。充分考虑各尺度分解层的系数特征,对低通子带,基于评价准则最优,采用免疫克隆选择优化策略迭代获取近似最优融合权值;对高通子带则选取绝对值最大作融合。实验结果表明：分别与基于小波、非下采样小波,以及Contourlet的融合结果相比较,文中融合算法获得的融合图像边缘的清晰度,以及整体的对比度都有所改善。     

程序设计语言的通用测试系统的设计

总结了现阶段常用程序设计教学语言的共性,在此基础上提出了程序设计语言通用测试系统的设计方案。方案扩展了试题库的含义和结构,针对通用平台的设计目标,提出了“插件式”的题库管理方案,简要介绍了以蓝图为中心的组卷策略和基于XML的蓝图结构,并阐述了程序设计的阅卷策略和系统实现过程中的若干关键技术。     

HVDC/GIC型直流偏磁的差异性分析

考虑到地磁感应电流(geomagnetically induced current, GIC)具有低频性,过去一直将其近似等效为高压直流输电(high voltage direct current, HVDC)诱发的不平衡电流进行研究。然而,与HVDC型直流偏磁相比,GIC型直流偏磁具有显著的随机性与时变性,因此简单地将两者完全等效处理并不合理,在特定场景下应加以区分。为此,首先,从理论上分析了两种类型直流偏磁在诱发原因及特点上的差异。其次,通过研究直流偏磁对变压器本体以及电流互感器的不利影响,进一步探究两种类型直流偏磁对电网一/二次设备的影响差异,为后续的偏磁治理提供有效参考。最后,基于PSCAD/EMTDC仿真平台搭建了等效仿真模型,并通过仿真验证了理论分析的正确性。     

基于空充暂态电流的线路保护CT极性校验方法

线路保护电流互感器(current transformer, CT)二次采样回路接线正确是保护正确动作的前提。针对线路投运启动过程中保护CT极性校验困难的问题,首先分析了在有限负荷和无负荷下利用线路稳态电流进行极性校验的边界限制条件。进而针对不满足限制条件下无负荷极性校验困难的问题,提出了一种基于空充暂态电流的线路保护CT极性校验方法。该方法先利用幅值较大的本端暂态电流对三相CT相对极性进行初判,再通过线路本端暂态电流和测量电压基于线路结构计算对端暂态电流来实现本端保护CT极性错误相识别,CT极性正确时对端计算电流理论上为零,CT极性错误时会呈现较大的幅值。该方法有效解决了传统极性校验方法在负荷不足时无法校验的问题。仿真和录波验证了该方法的有效性,并已纳入工程应用方案。     

结合感知损失与双重对抗网络的低剂量CT图像去噪

低剂量计算机断层扫描（LDCT）成像技术在医学诊断中得到广泛应用,但其斑纹噪声和非平稳条纹伪影复杂,目前多数算法仅依靠推断条件后验概率来实现图像去噪,无法应对LDCT图像噪声复杂、数据量少、先验知识缺乏的问题。提出一种结合感知损失的双重对抗网络去噪算法,以实现LDCT图像复原。该算法包含一个去噪器和一个生成器,分别从图像去噪和噪声生成2个角度来建模干净-噪声图像对的联合分布,通过联合学习使得去噪器和生成器相互指导,从而充分学习数据中的噪声信息和清晰图像信息,且学习到的去噪器可以直接用于LDCT图像修复。考虑到通过感知损失学习语义特征差异可以使去噪结果保留更多的细节和边缘信息,提出一种掩膜自监督方法,针对CT图像域训练一个语义特征提取网络用于计算感知损失。实验结果表明,与BM3D、RED-CNN、WGAN-VGG等主流去噪算法相比,该算法可以有效抑制噪声并去除伪影,最大程度地保留边缘轮廓和纹理细节,产生更符合人眼视觉特性的去噪效果,与当下LDCT图像去噪性能较好的SACNN算法相比,所提算法的PSNR和SSIM指标分别提升1.26 dB和1.8%。     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification (IAIEO-LCC) model. The proposed IAIEO-LCC technique initially performs Median Filtering (MF)-based pre-processing and data augmentation process. Besides, Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver. Moreover, VGG-19 based feature extractor and Equilibrium Optimizer (EO)-based hyperparameter tuning processes are also involved to derive the feature vectors. At last, Stacked Gated Recurrent Unit (SGRU) classifier is exploited to detect and classify the liver cancer effectively. In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance, a wide range of simulations was conducted and the results were inspected under different measures. The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.     

工业CT在航空工业的应用

随着飞机新型号的不断推出,航空工业对于产品的尺寸测量、缺陷检测和内部结构可视化等检测需求日益增多,传统的检测技术已无法满足这些高精度、高质量的需求。阐述了工业CT的原理、检测能力、影响检测能力的因素和工业CT的局限性与挑战。随后介绍了工业CT在增材制造、复合材料、飞机维修和航空发动机等方面的应用,简述了工业CT在尺寸形态、孔隙测量、逆向设计、三维缺陷、故障检测与诊断和壁厚测量等方面的优势与应用现状。工业CT能很好地解决目前航空工业中的检测难题,具有不受产品材料和形状限制的独特优势。