Fractional anisotropy‐weighted front evolution algorithm for white matter tractography based on diffusion tensor imaging data

Tractography is one of the most important applications of diffusion tensor imaging (DTI) which noninvasively reconstructs 3D trajectories of the white matter tracts. Because of the intravoxel orientation heterogeneity of DTI data, some of tractography algorithms are unable to follow the correct pathways after the crossing and branching regions. Front propagation techniques are efficient methods in tracking the crossing fibers. A key parameter influencing the performance of these algorithms is the cost function which is mainly based on the colinearity of tensors' eigenvectors. The effect of the eigenvalues on the anisotropy strength of tensor has not been previously addressed in the definition of the speed function. In this article, a new speed function, based on the effect of diffusion anisotropy and the colinearity of eigenvectors is proposed. The performance of the suggested method on fiber tracking and crossing fiber detection has been evaluated using synthetic datasets, and the feasibility of the proposed method was shown by fiber tracking implemented on real DTI data. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 307–314, 2011     

A new images segmentation method based on modified particle swarm optimization algorithm

The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 265–271, 2013     

基于双分支多尺度融合网络的毫米波SAR图像多目标语义分割方法

在毫米波合成孔径雷达(SAR)安检成像违禁品的检测与识别中,存在着目标尺寸过小、目标被部分遮挡和多目标之间重叠等复杂情况,不利于违禁品的准确识别。针对这些问题,提出了一种基于双分支多尺度融合网络(DBMFnet)的违禁品检测方法。该网络使用Encoder-Decoder的结构,在Encoder阶段,提出一种双分支并行特征提取网络(DBPFEN)来增强特征提取;在Decoder阶段,提出一种多尺度融合模块(MSFM)来提高对目标的检测能力。实验结果表明,该方法的均交并比(mIoU)均优于现有的语义分割方法,降低了漏检与错检率。

     

一种基于FCM的图像分割方法

提出一种新的图像分割方法 FWFCM(fast walvet fuzzy C-means method),该方法对图像像素点的灰度进行模糊隶属度的分析,将需要聚类的像素空间投影到灰度直方图空间,从而减少了经典FCM算法的迭代计算量,提高了算法的收敛速度;并且利用小波变换的多分辨率的分析,抑制噪声点对图像分割的影响,提高了图像分割的精度.     

一种基于谱减的声纳噪声自适应消除方法

噪声自适应消除是声纳信号处理的重要研究内容之一.传统的噪声自适应抵消算法需要单独的阵列(阵元)以获得不含期望信号的参考噪声信号,这在实际工程应用中往往是不现实的.提出在不增加阵元的情况下,通过相邻两个阵元输出信号进行加权处理,合成一路不包含给定方向信号的噪声信号;同时,借鉴语音信号处理中普遍应用的谱减降噪处理方法,达到...     

Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging (MRI) images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform (DWT) for pre‐ and post‐processing, fuzzy c‐means (FCM) for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background (BG). Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305–314, 2016     

A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques

Tissues in brain are the most complicated parts of our body, a clear examination and study are therefore required by a radiologist to identify the pathologies. Normal magnetic resonance (MR) scanner is capable of producing brain images with bounded tissues, where unique and segregated views of the tissues are required. A distinguished view upon the images is manually impossible and can be subjected to operator errors. With the assistance of a soft computing technique, an automated unique segmentation upon the brain tissues along with the identification of the tumor region can be effectively done. These functionalities assist the radiologist extensively. Several soft computing techniques have been proposed and one such technique which is being proposed is PSO‐based FCM algorithm. The results of the proposed algorithm is compared with fuzzy C‐means (FCM) and particle swarm optimization (PSO) algorithms using comparison factors such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy (energy function), Jaccard (Tanimoto Coefficient) index, dice overlap index and memory requirement for processing the algorithm. The efficiency of the PSO‐FCM algorithm is verified using the comparison factors.     

基于改进人工鱼群算法的机械故障聚类诊断方法

发展新的理论或方法快速准确地实现机械故障信号的聚类诊断是众多学者研究热点。由于人工鱼群优化算法具有结构简单,良好的并行性、快速性等特点,把人工鱼群优化算法引入机械故障诊断中。基于人工鱼群算法的基本原理提出了一种改进的人工鱼群追尾聚类算法,定义了相似度因子和聚类判别因子,建立了模拟人工鱼群追尾行为的机械故障聚类诊断模型,并将之应用于机械故障特征信息的聚类分析。实例分析表明了本文方法的有效性。     

基于前馈神经网络的自适应回声消除方法

回声消除常用的LMS算法收敛性差，而收敛性好的RLS算法计算量大。文章中提出一种全新的求解方法：基于前馈神经网络的自适应回声消除方法。把回声消除模型中求解滤波器系数的问题转化为前馈神经网络神经元权值的训练问题，并运用误差反向传播算法(BP算法)得出神经元权值的递推公式。经仿真计算，能较好地实现回声消除，与原传统算法LMS和RLS计算比较：该方法能得到非常高的计算精度和明显优越的收敛性能，而计算量只有RLS算法的一半。     

基于三阶累积量及自适应滤波时延估计的管道定位方法

针对直接互相关被动时延估计法定位管道异常振动事件存在噪声干扰影响定位精度的问题,提出了基于三阶累积量及自适应滤波时延估计的管道异常事件定位方法。该方法对顺、反两路异常振动信号进行三阶自累积量和互累积量估计,抑制高斯相关噪声和对称分布噪声。然后利用自适应滤波时延估计算法对三阶自累积量和互累积量信号的时延进行迭代计算,在不依赖先验知识的情况下抑制非高斯相关噪声。经现场实验证明,该方法可以准确地对管道异常事件进行定位,对噪声具有很好的抑制作用,改善了直接互相关时延估计的性能。相对于直接互相关时延估计方法,相对定位误差由2.7%降低到0.6%,定位一致性提高了三倍,平均定位精度可达14m。     

基于运动一致性的视频对象分割方法研究

视频运动对象检测和分割是图像处理中最具挑战性的问题之一。针对目前大部分分割算法相当复杂而且计算量大的问题，提出了一种基于运动一致性的视频对象分割方法。该方法从MPEG压缩码流中提取运动矢量场来分割视频对象，首先对运动矢量场进行滤波和校正，然后进行全局运动补偿得到对象的绝对运动矢量场，最后采用K-means聚类算法对运动矢量场进行聚类分析从而分割出感兴趣的视频运动对象。MPEG标准测试序列的试验结果证明，该方法是有效的。     

A variational level set segmentation formulation based on signal model for images in the presence of intensity inhomogeneity

Biological images with significant intensity inhomogeneity are considerably difficult for the tissue segmentation. To overcome the difficulties caused by the intensity inhomogeneity, this study presents a variational level set method to simultaneous bias field estimation and tissue segmentation for images in the presence of intensity inhomogeneity. An energy function is defined in terms of two data fitting terms which incorporate the local clustering properties into the global region information. First, depended on the observed image mode, the local cluster property based on the observed signal is simplified to a criterion function which is similar to the Mumford‐Shah model. The local criterion energy is then integrated with a global region measure, which is based on intensity difference of the true signal. The energy is minimized in a variational level set formulation with a regularity term, thus avoiding the expensive computation of the level set reinitialization and keeping the curve close to the signal distance function. Experiment results on biological images show desirable performance and demonstrate the effectiveness of the proposed algorithm.     

An improved adaptive detail enhancement algorithm for infrared images based on guided image filter

Detail enhancement algorithms are important for raw infrared images to improve their overall contrast and highlight important information in them. To solve the problems that current algorithms like GF&DDE have, an improved adaptive detail enhancement algorithm for infrared images based on a guided image filter is proposed in this paper. It chooses the threshold for the base layer image adaptively according to the histogram statistical information and adjusts the mapping range of the histograms according to the dynamic range of the image. Besides, the detail layer is handled by a simpler adaptive gain control method to achieve the good detail enhancement effect. Finally, the base layer and the detail are merged according to the approximate proportion of the background and the details. Experimental results show that the proposed algorithm can adaptively and efficiently enhance different dynamic range images in different scenarios. Moreover, this algorithm has high real-time performance.     

FPGA implementation of particle swarm optimization based on new fitness function for MRI images segmentation

Magnetic resonance imaging (MRI) is considered as a key part in therapeutic procedures because it clearly defines the aim. It also avoids sensitive organs and it determines the desired paths. This phenomenon requires image processing operations such as segmentation to locate the tumor. Medical image segmentation is still an important topic in the field of brain tumor. In the present article, we propose a Hardware Architecture of segmentation based on a Modified Particle Swarm Optimization (HAMPSO) algorithm for MRI images segmentation. To achieve this, we use the Xilinx System Generator (XSG) to be implemented on a Field Programmable Gate Array (FPGA). This architecture is based on a new variant of objective function. These performances of the proposed method are proved using a set of MRI images and were compared to the Hardware Architecture of segmentation based on Particle Swarm Optimization (HAPSO) in terms of either device utilization, execution time, qualitatively or quantitatively results.     

基于FLANN的动态称重法

根据“逆模型”的思想 ,利用神经元网络良好的逼近能力 ,基于函数联接型神经网络 ( FLANN)的传感器动态补偿方法和最小二乘法 ,提出了一种车辆动态称重解决新方法     

基于超声图像特征的肝硬化分析系统

文章首先研究了肝脏M型超声图像和B型超声图像的特征提取，提取了肝脏M型超声图像的运动包络、相隔一定时间的A型扫描线对的相关特征系数以及肝脏B型超声图像的共25个特征参数。在已提取的特征参数中，用特征融合的方法筛选出对肝硬化最敏感的特征组，并利用这20个参数的Fisher判据建立了基于超声图像特征的肝硬化无损自动分析系统。对临床43例的应用表明：该定量分析系统对肝硬化的诊断具有良好的效果。     

适用于离焦状态相机的相位编码标定方法

现阶段通用的相机标定方法需要使用者提供准确的标定板特征点像素坐标。但对某些成像位置特殊的相机而言,一般标定物(如长宽在厘米级别的标定板)的使用范围在其清晰成像范围之外。使用这些相机拍摄一般标定物,只能得到离焦的模糊图像,无法准确提取特征点像素坐标。本文分析了光学系统离焦状态对基于正弦光栅的结构光(简称正弦结构光)相位的影响。利用正弦结构光相位与相机对焦状态无关的性质,提出了一种利用相移法正弦结构光编码的方法,对标定物上特征点进行相位编码,实现了相机在离焦状态下的标定。经过实验验证,标定结果焦距长度与真实值之间最大偏差为0.47%,最大像素重投影误差为0.17 pixels。该方法为具有特殊成像范围的相机的标定提供了一种解决方案。     

基于改进有序聚类法的立式加工中心进给系统温测点优化

为解决立式加工中心热误差补偿关键技术中温测点难选取的问题,提出了一种基于改进有序聚类法的机床进给系统温测点优化方法。首先,结合试验数据计算反映温测点温度变量与热误差相关性的互信息值,初步筛选机床各部件的温测点,消除测点间的耦合性;然后,根据筛选出的温测点,通过建立类直径矩阵和计算各类的最小误差函数,获得温度变量分类;最后,基于多元线性回归建立包含多个不同温测点的热误差模型,并对模型进行统计学综合分析,确定了最佳聚类数和最佳温测点。结果表明：在不同加工条件下采用改进有序聚类法建立的热误差模型的均方根误差和平均残差分别降至1.05 μm和1 μm以下,相较于采用传统有序聚类法和灰色关联度模糊聚类法建立的热误差模型,它具有更高的热误差预测精度和更好的鲁棒性。所提方法在中小型加工中心进给系统的温测点研究中具有广阔的应用前景。     

复相关法测频的硬件实现

复相关算法适用于多普勒频移的测量,通过选用FPGA作为硬件实现平台,可以满足实际的多普勒测频工作。根据复相关算法测频原理和硬件平台的实际特点,分别对反正切、低通滤波器和正交变换进行了优化,降低了硬件的消耗。高效的反正切函数算法通过预先存入值省去了每次反正切函数的运算,优化的FIR滤波器和正交变换器进一步节约了查找表（LUT）资源。通过仿真实验,结合数据的质量保证和控制算法,结果验证了硬件平台的可行性。     

最小均方电液负载模拟器加载系统控制研究

在电液负载模拟器进行动态力加载时,对其快速性指标有更高要求,此时加载误差比较大的问题就凸显出来,即输出加载力的数值随加载频率的增大而增大、相位也有超前,针对加载系统的这一性能特殊变化规律与实际现象,提出基于最小均方算法的自适应神经网络控制策略。该方法对神经网络的配置权值进行实时在线调整,调整效率和算法的收敛性都有明显提高,可以有效减小控制系统工作时的循环调整时间。当输入信号经过加权运算后作用于加载控制系统时,加载误差明显减小,加载系统的快速性与跟踪精度进一步提高。仿真和实验结果表明,采用文中方法后,加载力输出的幅值增大和相位超前量得以抑制,系统动态力加载时的综合性能有所提高,所提出算法调整规则有效。