电阻抗扫描成像病灶参数提取算法

为了提升电阻抗扫描成像的性能,本文提出了一个完备参数提取算法.该算法由前向问题建模以及参数提取两部分构成.前向问题模型基于静电散射理论,通过该模型可以获得前向问题的解析解.与常用的采用迭代过程的前向问题求解方法相比,采用新模型的前向问题求解速度大大提高.基于提出的前向问题模型,参数提取可以视作一个约束优化问题并采用改进的单纯形算法求解.仿真实验表明,完备参数提取算法与已有的参数或信息提取算法相比,不仅能够获得乳腺癌病灶更多的信息,并且准确度和鲁棒性更好.     

电阻抗扫描成像完备参数提取算法

为了提升电阻抗扫描成像的性能,本文提出了一个完备参数提取算法。该算法由前向问题建模以及参数提取两部分构成。前向问题模型基于静电散射理论,通过该模型可以获得前向问题的解析解。与常用的采用迭代过程的前向问题求解方法相比,采用新模型的前向问题求解速度大大提高。基于提出的前向问题模型,参数提取可以视作一个约束优化问题并采用改进的单纯形算法求解。仿真实验表明,完备参数提取算法与已有的参数或信息提取算法相比,不仅能够获得乳腺癌病灶更多的信息,并且准确度和鲁棒性更好。     

一种改善空间分辨率的电阻抗成像技术

电阻抗断层功能成像技术(Electrical Impedance Tomography简称EIT),是当今生物医学工程学重大研究课题之一,它是通过对物体表面电压、电流的测量来重建物体内部阻抗分布或变化的一种新颖的医学成像技术.它是继形态、结构成像之后,于近20年才出现的新一代无损伤功能成像技术.本文介绍了电阻抗成像技术的基本原理及研究的现状,针对成像技术中所需的有限元方法,采用了对某一物体内部阻抗变化的位置进行局部细化剖分,再重建出物体内阻抗分布图象的成像方法.这种方法不仅能减少重建算法的计算量,且能提高阻抗图象的空间分辨率.实验结果表明这种方法是可取的.     

基于LabVIEW的等位线反投影算法研究

本文分析了一种基于等位线反投影算法的电阻抗成像方法,并用虚拟仪器LabVIEW软件构建一个虚拟平台,实现了电阻抗成像的图像重建。结果表明在各种电导率分布下,该等位线反投影算法可以准确的对目标进行定位。     

一种快速电阻抗成像方法及其灵敏度研究

该文介绍了一种基于等位线反投影算法的快速电阻抗断层成像方法，并实现了一种以等位线反投影算法为基础的实用动态电阻抗断层成像方法，分别研究了简单、复杂场域电导率分布下，该重建算法对场域内目标的定位精度及其对场域内部电导率分布变化的灵敏度。研究结果表明：该重建算法可以准确对目标进行定位，而且成像速度快，具有一定的分辨率，对场域内电导率分布的变化具有较好的灵敏度，但该重建算法的图像分辨率仍有待进～步提高。该快速电阻抗成像方法非常适合于对脑血肿、脑水肿患者的实时图像监护，以帮助医生及时做出正确的诊断，这在医学上有着重要的意义。     

电阻抗断层成像的神经网络模型

由于传统的电阻抗断层成像算法成像速度慢、且在某些情况下得到的图像无法反映敏感场的真实阻抗分布,本文提出了两种基于不同神经网络(BP神经网络和RBF神经网络)的电阻抗图像重建模型及其成像算法.结果表明,基于神经网络模型的成像算法从成像质量和速度上均优于传统的成像算法.     

无解调电阻抗成像方法研究

探讨了正交序列数字解调算法中参考信号与测量信号频率不一致引起的解调结果不准确影响成像质量的问题,提出无需数字解调直接提取幅值进行电阻抗成像的方法：通过多次采样比较和算术平均滤波法获取幅值进行EIT成像。通过构建以FPGA为核心控制器的EIT系统,验证了直接提取阻抗幅值方法的可行性。实验结果表明：系统测量精度达0.082%,信噪比为60.3 dB,能准显示物体的大小和位置,空间分辨率达到0.29%,验证了方法的可行性,提高了数据采集精度和系统的稳定性,改善了图像成像质量,该方法为EIT技术的完善及系统性能的提升提供了一种有效的解决方案。     

电阻抗成像中Jacobi矩阵的一种快速仿真算法

电阻抗成像(FIT)是一种新兴的计算机重构成像技术，它根据物体内部不同物质的导电参数(如电阻率、电容率)的不同，通过对物体表面电流、电压的施加及测量来获知物体内部导电参数的分布，进而重构出反映物体内部结构的仿真图像。作为一种数学物理反问题，EIT技术具有其本身的特点和难点，因而目前还处于探索性研究阶段。而其中涉及到的Jaeobi矩阵计算是大多数EIT重构算法中最重要的环节之一。该文基于微分原理推导了一种该矩阵的快速仿真算法，与其它算法相比，本算法在计算速度和精度上都有很大提高，并且物理概念更清晰、适用范围更广。这对EIT技术走向实用化具有积极意义。     

合成孔径与实孔径雷达谱域成像算法对比分析

讨论了合成孔径雷达(Synthetic aperture radar,SAR)和实孔径雷达(Real aperture radar,RAR)一维扫描方式下的谱域成像实现问题.文中从SAR和RAR扫描下的波数域波散关系入手,分析了这两种扫描方式下的谱域填充区域和成像分辨率,指出了二者的异同,导出了相应的成像算法.单目标和组合目标的雷达成像仿真实验验证了两种扫描方式下成像算法的有效性和理论分析结果.     

128电极电阻抗断层成像数据采集系统设计

以实现旋转电极法电阻抗断层成像数据采集自动化为目的,设计开发了一种基于NIOS Ⅱ处理器、拥有128个电极的旋转电极法电阻抗断层成像数据采集系统.进行了数据采集实验,在PC机上获得了采集结果,验证了系统的可靠性.     

说话人识别中基于Fisher比的特征组合方法

为了提高说话人识别的准确率,可以同时采用多个特征参数,针对综合特征参数中各维分量对识别结果的影响可能不一样,同等对待并不一定是最优的方案这个问题,提出基于Fisher准则的梅尔频率倒谱系数(MFCC)、线性预测梅尔倒谱系数(LPMFCC)、Teager能量算子倒谱参数(TEOCC)相混合的特征参数提取方法。首先,提取语音信号的MFCC、LPMFCC和TEOCC三种参数;然后,计算MFCC和LPMFCC参数中各维分量的Fisher比,分别选出六个Fisher比高的分量与TEOCC参数组合成混合特征参数;最后,采用TIMIT语音库和NOISEX-92噪声库进行说话人识别实验。仿真实验表明,所提方法与MFCC、LPMFCC、MFCC+LPMFCC、基于Fisher比的梅尔倒谱系数混合特征提取方法以及基于主成分分析(PCA)的特征抽取方法相比,在采用高斯混合模型(GMM)和BP神经网络的平均识别率在纯净语音环境下分别提高了21.65个百分点、18.39个百分点、15.61个百分点、15.01个百分点与22.70个百分点;在30 dB噪声环境下,则分别提升了15.15个百分点、10.81个百分点、8.69个百分点、7.64个百分点与17.76个百分点。实验结果表明,该混合特征参数能够有效提高说话人识别率,且具有更好的鲁棒性。     

一种激光诱导荧光光谱特征提取新方法

利用主成分分析（Principal Component Analysis,PCA）和Fisher线性判别分析（Fisher Linear Discriminative Analysis,FLDA）方法相结合提取特征,提出了一种荧光光谱特征提取新方法——PCA_FLDA。实验证明,新方法提高了激光诱导自体荧光光谱对早期结肠癌的诊断精度。对预处理后的240条荧光光谱,利用PCA_FLDA算法提取了50个特征变量,利用支持向量机将其分为正常组织和癌变组织,分类敏感性、特异性和准确率可分别达到97.5%、95.12%和96.25%。     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and edema regions inference from brain magnetic resonance imaging (MRI) information is considered to be great challenge due to brain tumors complex structure, blurred borders, besides exterior features like noise. The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation. Combined form of Median Modified Wiener filter (CMMWF) is chiefly deployed for denoising, and morphological operations which in turn eliminate nonbrain tissue, efficiently dropping technique’s sensitivity to noise. The proposed system contains the main phases such as preprocessing, brain tumor extraction and post processing. Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering (IPFC) algorithm. The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction. Then, the post processing of images are done through morphological operations along with Hybrid Median filtering (HMF) for attaining exact tumors representations. Additionally, suggested algorithm is substantiated by comparing with other existing segmentation algorithms. The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy, sensitivity, specificity, and recall.     

低尺度血管检测在视网膜血管分割中的应用

视网膜图像分析成为目前诊断多种疾病非侵入的主要方式,其中血管的提取是最重要的一步。监督学习的方法在血管提取上有很好的效果,为了进一步提高检测的精度,提出了低尺度血管检测(LVD)算法。该网络除了有一个提取输入原尺度下特征的子网络外,还增加了两个低尺度下的特征提取子网络,并将低尺度下的单一输出融合原尺寸下的特征,降维后得到最后的输出结果。考虑到眼底血管结构特性,在LVD中设计了具有较深层数和较少参数的非对称固定深度子网络(ADS)。在公共的数据库DRIVE中进行测试,仅采用彩色眼底图像的绿色分量和B-COSFIRE滤波响应作为特征输入,其敏感性、特异性、准确率以及AUC指标分别为0.819 2、0.984 2、0.969 5、0.978 2,达到了先进水平。     

Automated breast cancer detection and classification using ultrasound images: A survey

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.     

Quantitative evaluation of a wavelet-based method in ventricular late potential detection

Ventricular late potentials (VLPs) are low-amplitude, high-frequency waveforms appearing in the terminal part of the QRS complex in electrocardiogram (ECG) of patients who are susceptible to ventricular tachycardia and sudden cardiac death, after surviving myocardial infarction. Accordingly, VLP detection presents a prominent non-invasive marker for some cardiac diseases clinically. This paper proposes a VLP detection method based on the wavelet transform and investigates its performance. In this method, a modified vector magnitude waveform is formed using discrete wavelet transform for each high-resolution ECG (HRECG) record; then, by applying the continuous wavelet transform to the QRS complex end part in this waveform, a feature vector is extracted from the resultant time-scale plot. This wavelet-based feature vector is processed by principle component analysis to reduce its dimensionality. Finally, a supervised feedforward artificial neural network, trained by a proper set of these feature vectors, is employed as a classifier. To evaluate the proposed method performance, a HRECG database consisting of the real VLP-negative and simulated VLP-positive patterns is used. In a comparative approach, different VLP detection techniques including the conventional time-domain method, developed by Simson, and some methods utilizing distinct diagnostic features are also applied to this database to investigate the capability of the proposed method in VLP analysis more completely. The results show the proposed method, employing the wavelet transform in both pre-processing and feature extraction stages, reveals high evaluation criteria (accuracy, sensitivity, and specificity) and is qualified to detect VLPs.     

基于蚁群优化算法的精密伺服转台故障诊断方法

提出了一种基于蚁群优化算法的精密伺服转台故障诊断方法. 根据现场观测建立了转台系统故障特征模式库. 利用蚁群优化算法求解故障特征模式的最优分类问题, 并定义敏感度和明确度来评价蚁群搜索到的诊断规则的分类性能, 以减少故障特征信息中的冗余信息, 使诊断规则得到约简. 对某精密伺服转台的若干类故障诊断结果表明, 该方法具有收敛速度快、鲁棒性强、诊断精度高和结果可靠等优点.     

A new approach to evaluate spatiotemporal dynamics of controlling parameters in distributed environmental models

Distributed environmental models are usually high-dimensional and non-linear. To comprehensively evaluate the spatiotemporal dynamics of model controls, we propose a novel multi-step approach based on Sobol's method to evaluate parameter sensitivity as well as interactions with respect to different model outlet points, using different objective functions to assess different hydrodynamic conditions; all varying through time. This complete sensitivity analysis can be performed for prior and posterior parameter ranges. The difference between them can be used to assess the influence of parameter constraints on the results of sensitivity analyses. We applied this holistic approach to an existing distributed karst watershed model. The results demonstrated that 1) a limited number of spatially-distributed parameters control the varying flow pattern, 2) the model is nonlinear and the influential parameters are highly correlated in the model domain and 3) the spatial patterns of identified parameter sensitivity and interactions are strongly influenced by the specified parameter bounds.     

基于参数优化VMD和样本熵的滚动轴承故障诊断EI北大核心CSCD

针对滚动轴承故障特征提取不丰富而导致的诊断识别率低的情况,提出了基于参数优化变分模态分解(Variational mode decomposition,VMD)和样本熵的特征提取方法,采用支持向量机(Support vector machine,SVM)进行故障识别.VMD方法的分解效果受限于分解个数和惩罚因子的选取,本文分析了这两个影响参数选取的不规律性,采用遗传变异粒子群算法进行参数优化,利用参数优化的VMD方法处理故障信号.样本熵在衡量滚动轴承振动信号的复杂度时,得到的熵值并不总是和信号的复杂度相关,故结合滚动轴承的故障机理,提出基于滚动轴承故障机理的样本熵,此样本熵衡量振动信号的复杂度与机理分析的结果一致.仿真实验表明,利用本文提出的特征提取方法,滚动轴承的故障诊断准确率有明显的提高.