Medical image fusion based on laws of texture energy measures in stationary wavelet transform domain

Medical image fusion is widely used in various clinical procedures for the precise diagnosis of a disease. Image fusion procedures are used to assist real-time image-guided surgery. These procedures demand more accuracy and less computational complexity in modern diagnostics. Through the present work, we proposed a novel image fusion method based on stationary wavelet transform (SWT) and texture energy measures (TEMs) to address poor contrast and high-computational complexity issues of fusion outcomes. SWT extracts approximate and detail information of source images. TEMs have the capability to capture various features of the image. These are considered for fusion of approximate information. In addition, the morphological operations are used to refine the fusion process. Datasets consisting of images of seven patients suffering from neurological disorders are used in this study. Quantitative comparison of fusion results with visual information fidelity-based image fusion quality metric, ratio of spatial frequency error, edge information-based image fusion quality metric, and structural similarity index-based image fusion quality metrics proved the superiority. Also, the proposed method is superior in terms of average execution time to state-of-the-art image fusion methods. The proposed work can be extended for fusion of other imaging modalities like fusion of functional image with an anatomical image. Suitability of the fused images by the proposed method for image analysis tasks needs to be studied.     

High compressive ghost imaging method based on discrete cosine transform using weight coefficient matching

ABSTRACT

Ghost imaging can capture a scene without directly catching sight of the target, but in the case of high compression ratio, high-quality imaging is challenging at present. Here a ghost imaging method using weight coefficient matching based on discrete cosine transform (DCT) is proposed, in which the high-quality target images can be retrieved by obtaining the larger weight value in one-dimensional (1D) DCT spectrum. In the case of low sampling, the proposed method can not only acquire the spectral coefficients with large weight, but also put them in the correct position; eventually it can obtain the desired image by inverse discrete cosine transform of the spectrum. At the same sampling ratio, both simulation results and optical experiments show that the reconstructed image quality of the proposed method exhibits better performance. In addition, even the sampling ratio is as low as about 3%, the outline of the target image can still be roughly recognized.     

基于涡流成像的碳纤维增强树脂基复合材料细观结构可视化

对碳纤维增强树脂基复合材料（CFRP）的细观结构成像方法进行了研究,利用涡流成像技术实现了CFRP层合板中纤维方向及纤维缺失、褶皱和空隙过大等缺陷的可视化。首先通过有限元仿真和电路理论分析了CFRP板中涡流的生成机制和分布特性,阐述了基于涡流法的CFRP细观结构成像机制。然后介绍了用于扫描成像的高频涡流检测（HF-ECT）实验系统并确定了涡流探头的形式及其参数。最后利用涡流成像技术分别对单层板、正交层合板和四方向斜交层合板进行了检测,绘制了涡流检测（ECT）信号的三维伪彩图并得到了清晰的纤维纹路分布。通过引入滤波去噪技术和二维快速傅里叶变换（2D-FFT）对图像进行进一步处理,提高了图像分辨率并完成了不同方向上纤维纹路的分离,从而实现对层合板每单向层中缺陷的精确定位。     

A steganographic approach for secure communication of medical images based on the DCT-SVD and the compressed sensing (CS) theory

In this paper, a hybrid approach based on cryptography and steganography is proposed for the security of medical image over an open communication channel. In this approach, medical image information is embedded using discrete cosine transform (DCT)-singular value decomposition (SVD)-based embedding process. The medical image is encrypted using CS encryption before embedding into the standard image. This encrypted medical image is inserted into the singular values of DCT coefficients of the standard image to get the stego image. The experimental results show that the encrypted medical image is successfully extracted from the stego image under various image processing attacks at recovering side. The result analysis also shows improved imperceptibility of the stego image with a peak signal-to-noise ratio value above 60 dB for all types of medical images under consideration. Furthermore, the computation time of proposed approach is less than that of existing approaches.     

基于子波域空间相关的多分辨图像滤波方法

从静态子波变换入手,提出了一种有效的图像滤波算法。通过计算相邻尺度下细节信号的空间相关性来区分噪声和信号,如果子波系数的空间相关性大,则认为此位置的系数含有特征及边缘信息予以保留。实现这种理论的完整方法包括两部分:空间滤波和子波收缩。仿真结果表明这个算法有很好的降噪性能。     

基于频域合成孔径聚焦的输电线路钢管杆超声导波成像检测

输电线路钢管杆结构是一种带有拔梢斜度的大口径薄壁管,超声导波周向B扫描检测传感器激发的超声导波在钢管杆传播过程中存在波束扩散现象。为了解决钢管杆超声导波检测过程中波束扩散导致周向检测分辨率降低的问题,提出了一种钢管杆超声导波频域合成孔径聚焦方法(Synthetic Aperture Focusing Technique, SAFT)。采用文中构建的超声导波合成孔径成像检测装置对直径为608 mm 的钢管杆进行超声导波扫查,可获得周B扫描图像和频域合成孔径聚焦图像。对比钢管杆周向B扫描图像和频域合成孔径聚焦图像,频域合成孔径聚焦图像中散射体和通孔的周向检测分辨率明显优于B扫描图像,文章提出的钢管杆超声导波频域合成孔径聚焦方法能够显著改善超声导波周向检测分辨率。     

基于激光热激励红外热成像纤维增强聚合物复合材料加固混凝土结构界面损伤无损检测

红外热成像（IRT）检测技术常被用于纤维增强聚合物复合材料（FRP）加固混凝土板的剥离损伤无损检测。但传统热源激励IRT法受加热距离短、热灵敏度低、功耗大等因素影响较大。本文提出了基于光学激发线激光热源的IRT法对FRP加固混凝土的剥离检测方法,该方法通过控制线热束在FRP加固混凝土结构的表面形成移动扫描热激励,通过红外热像仪测量结构中的界面剥离引起的表面局部热分布异常。基于模拟和实验研究结果,证明该方法对FRP加固混凝土结构中界面剥离检测具有如下优势:利用激光扫描热成像技术检测FRP改造混凝土结构中剥离损伤的可行性;实现了FRP加固混凝土结构的远距离、高热灵敏度及低功耗的损伤检测。      

基于独立分量分析的高速微铣削力混合信号噪声分离方法

为得到高速微铣削力的真实信号,并且准确识别各激励源,对微铣削力混合信号分离和识别技术进行研究。首先对铣削力混合信号矩阵进行预处理,利用对预处理结果的独立成分分析(ICA)分离得到独立源信号矩阵,再通过快速傅里叶变换(FFT)得到独立源信号的频谱,最后分析并结合实验工况识别出微铣削力信号、机械噪声信号和环境噪声信号。实验结果表明:该方法具有同时分离非高斯性的机械噪声信号和高斯性的环境噪声信号的优点,可以弥补传统方法只能抑制高斯噪声信号的不足。     

基于小波域多分辨率MRF的声呐图像目标分割

声呐图像受噪声影响严重,分辨率低,传统算法对其目标分割效果较差,为此,提出了小波域多分辨率MRF模型的声呐图像分割算法。小波域多分辨率分析有利于提取声呐图像弱特征信息;每一分辨率中的观测特征采用高斯混合模型建模,尺度内同标记的观测特征用高斯模型建模,用各向同性的双点多级逻辑(Multi-Level Logistic,MLL)模型建模每一尺度的标记场;最后,用迭代条件模式(Iterated Conditional Mode,ICM)实现多分辨率马尔可夫随机场(Multi-Resolution Markov Random Field,MRA-MRF)中能量函数的最优解,获取标记场,完成声呐图像分割。从视觉效果和定量分析两方面验证。对比实验的结果表明,该文算法能有效地提取声呐图像的弱目标信息,较好地将目标区域和背景区域分割出来,具有较高的分割精度和鲁棒性。     

Numerical fatigue life analysis of a high frequency mechanical impact treated industrial component based on damage mechanics models

After post weld treatment with high frequency mechanical impact (HFMI) treatment of welds, a significant increase of fatigue life (up to a factor of 10) can be achieved. During the last years numerous experimental tests of welded joints with simple geometry under constant amplitude loading have been performed to quantify the positive effect of high frequency mechanical impact treatment. Due to the lack of methods for the prediction of the high frequency mechanical impact benefits, a widespread use of this process is not the case yet. Furthermore, it is still not clear if the results of these fatigue tests can be transferred to complex geometries and complex loading conditions such as in industrial applications. Therefore, an approach to assess the fatigue life of complex welded structures under variable amplitude loading was developed. For this purpose, high frequency mechanical impact treatment and fatigue load of simple welded specimen made of S690QL steel were simulated with finite element analysis (FEA) firstly. Then, the needed damage parameters for the fatigue life correlation were evaluated from the finite element post‐processing. The calculated life time to crack initiation was in good agreement with the experimental fatigue test results. In the next step, this procedure was implemented on a welded arm of an evacuator of type EW180B of the company Volvo Construction Equipment made of S700MC. The variable amplitude load measured under real service condition was transferred to single constant amplitude load cycles using a rainflow‐counting algorithm. By simulation and damage mechanics evaluation of each load cycle the total damage sum could be calculated and compared with the experimental results from Volvo Construction Equipment.     

An extended CREAM model based on analytic network process under the type-2 fuzzy environment for human reliability analysis in the high-speed train operation

Human error is one of the largest contributing factors to unsafe operation and accidents in high-speed train operation. As a well-known second-generation human reliability analysis (HRA) technique, the cognitive reliability and error analysis method (CREAM) has been introduced to address HRA problems in various fields. Nevertheless, current CREAM models are insufficient to deal with the HRA problem that need to consider the interdependencies between the Common Performance Conditions (CPCs) and determine the weights of these CPCs, simultaneously. Hence, the purpose of this paper is to develop a hybrid HRA model by integrating CREAM, the interval type-2 fuzzy sets, and analytic network process (ANP) to overcome this drawback. Firstly, the interval type-2 fuzzy sets are utilized to express the highly uncertain information of CPCs. Secondly, the ANP is incorporated into the CREAM to depict the interdependencies between the CPCs and determine their weights. Furthermore, human error probability (HEP) can be calculated based on the obtained weights. Finally, an illustrative example of the HRA problem in high-speed train operation is proposed to demonstrate the application and validity of the proposed HRA model. The results indicate that experts prefer to express their preferences by fuzzy sets rather than crisp values, and the interdependences between the CPCs can be better depicted in the proposed model.