Multifractal analysis of the UV/VIS spectra of malignant ascites: Confirmation of the diagnostic validity of a clinically evaluated spectral analysis

Multifractal (MF) approach was applied for the analysis of ultraviolet/visible (UV/VIS) spectra as an independent confirmation of the diagnostic efficacy of UV/VIS spectral analysis of intraperitoneal fluids, ascites, taken from patients with a known clinical diagnosis. Recently, it was reported that from UV/VIS spectra differentiation of malignant from benign ascites is possible. Here, it was shown that by using MF analysis of UV/VIS spectra, the objective classification of UV/VIS spectra is possible. The applicability of UV/VIS analysis and MF classification of spectra were evaluated on N=68 cases, of which M=64 and B=4 were clinically confirmed as malignant and benign, respectively. The overall diagnostic efficacy was 89.71% when using on-line analysis of UV/VIS spectra (61 out of 68 samples were positively recognized: 58 malignant and 3 benign), and even 95.59% by using off-line MF classsification (65 out of 68 samples were classified correctly: 63 malignant and 2 benign). It can be inferred that UV/VIS spectral analysis of ascites, combined with MF analysis, could be suggested as a successful and safe screening method in the evaluation of intraperitoneal fluids.     

Are signal intensity and homogeneity useful parameters for distinguishing between benign and malignant soft tissue masses on MR images?: Objective evaluation by means of texture analysis

Objectives

To objectively identify possible differences in the signal characteristics of benign and malignant soft tissue masses (STM) on magnetic resonance (MR) images by means of texture analysis and to determine the value of these differences for computer-assisted lesion classification.

Method

Fifty-eight patients with histologically proven STM (benign, n=30; malignant, n=28) were included. STM texture was analyzed on routine T1-weighted, T2-weighted and short tau inversion recovery (STIR) images obtained with heterogeneous acquisition protocols. Fisher coefficients (F) and the probability of classification error and average correlation coefficients (POE+ACC) were calculated to identify the most discriminative texture features for separation of benign and malignant STM. F>1 indicated adequate discriminative power of texture features. Based on the texture features, computer-assisted classification of the STM by means of k-nearest-neighbor (k-NN) and artificial neural network (ANN) classification was performed, and accuracy, sensitivity and specificity were calculated.

Results

Discriminative power was only adequate for two texture features, derived from the gray-level histogram of the STIR images (first and 10th gray-level percentiles). Accordingly, the best results of STM classification were achieved using texture information from STIR images, with an accuracy of 75.0% (sensitivity, 71.4%; specificity, 78.3%) for the k-NN classifier, and an accuracy of 90.5% (sensitivity, 91.1%; specificity, 90.0%) for the ANN classifier.

Conclusion

Texture analysis revealed only small differences in the signal characteristics of benign and malignant STM on routine MR images. Computer-assisted pattern recognition algorithms may aid in the characterization of STM, but more data is necessary to confirm their clinical value.     

基于长焦区聚焦换能器的扫描光声乳腺成像技术

报道了一种利用长焦区聚焦超声换能器检测光声信号的扫描光声乳腺成像系统.通过增加聚焦换能器焦区的长度,该技术可以快速实现对大块组织(如乳腺)的光声成像.测试结果表明,该实验系统能够对大块模拟样品中吸收体的位置、尺寸以及光能量吸收情况实现较为准确的成像.另外,实验中,该系统还可便捷地与传统的超声技术相结合,从而实现多模式复合的乳腺癌诊断.初步结果显示了该技术路线在乳腺癌早期诊断应用上的前景. 关键词： 乳腺癌诊断 光声成像 扫描光声层析术 脉冲回波法     

Comparisons of MFDFA,EMD and WT by Neural Network,Mahalanobis Distance and SVM in Fault Diagnosis of Gearboxes

A method for gearbox fault diagnosis consists of feature extraction and fault identification. Many methods for feature extraction have been devised for exposing nature of vibration data of a defective gearbox. In addition, features extracted from gearbox vibration data are identified by various classifiers. However, existing literatures leave much to be desired in assessing performance of different combinatorial methods for gearbox fault diagnosis. To this end, this paper evaluated performance of several typical combinatorial methods for gearbox fault diagnosis by associating each of multifractal detrended fluctuation analysis (MFDFA), empirical mode decomposition (EMD) and wavelet transform (WT) with each of neural network (NN), Mahalanobis distance decision rules (MDDR) and support vector machine (SVM). Following this, performance of different combinatorial methods was compared using a group of gearbox vibration data containing slightly different fault patterns. The results indicate that MFDFA performs better in feature extraction of gearbox vibration data and SVM does the same in fault identification. Naturally, the method associating MFDFA with SVM shows huge potential for fault diagnosis of gearboxes. As a result, this paper can provide some useful information on construction of a method for gearbox fault diagnosis.     

惯性约束聚变靶场激光束精密调焦及诊断技术

 主要介绍了惯性约束聚变靶场精密调焦和诊断的实验系统,阐述了系统采用的谐波驱动器、调焦判据、图象处理等关键技术,实验中通过计算机控制采集到一系列轴向位移和相应焦斑面积的数据,可拟合出位移面积的函数变化曲线, 根据此曲线便可快速准确地确定焦面位置。     

光学相干层析成象技术的医学诊断应用

利用超短脉冲飞秒激光建立了OCT实验装置,通过对动物肾脏组织样品的层析成象,OCT图象与传统组织学图谱能找到很好的对应关系;对动物脑梗塞模型组织的扫描成象,得到了脑梗塞侧和正常侧的OCT图象,实验中,大脑病变组织和正常组织能够被正确区分。     

Research on Haar Spectrum in fault diagnosis of rotating machinery

In this paper,Haar Transform(HT)is used in the fault diagnosis of rotatingmachinery,and the“Impulse Sharpness”is presented as a diagnostic index.At present,Fourier Spectrumanalysis is most widely used Compared with FFT,HT is more rapid incomputation and more effective in discrete approximation.It’s very suitable for theextraction of pulses in the signal.However,HT has some shortcomings.It’s greatlyaffected by the starting point and length of the sample.Here,the authors present a methodto improve the stability and comparability of Haar Spectrum.The fault imitating test ofrolling bearing is carried out,and the results obtained have verified the sensitivity of HaarSpectrum and Impulse Index to the fault.     

The generalized variational principles with their applications in structural analysis

This paper will chseuss the generalized variational prinetples.which the established bythe methed of undetermined multipliers in structures.and analys the statiedllyindeterminate truss.in which these principles will be used.At the same time we bring asymmetrical matrix and give speeitie sohution.thus all the internal forees of structures maybe found.     

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.     

剪切波弹性成像定量参数对良恶性乳腺肿块的鉴别及对乳腺癌术后复发的预测价值

探讨剪切波弹性成像定量参数对乳腺癌诊断和预后预测的作用.以乳腺癌患者60例作为观察组,乳腺良性结节患者60例作为对照组,利用剪切波弹性成像行超声检查,收集最大弹性模量值(Emax).t检验分析两组Emax差异,并使用ROC曲线分析Emax诊断乳腺癌的价值.结果显示,与对照组比较,观察组AE-max、Shell1Emax...