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     

Divide et impera: Acceleration of DTI tractography using multi‐GPU parallel processing

Diffusion tensor imaging (DTI) tractography technique represents neural fiber pathways by using local tensor information based on water diffusion anisotropy in brain white matter. However, DTI tractography is often unable to reconstruct crossing, kissing, and branching fiber trajectories due to intrinsic limitations of DTI. Increasingly complex tractography algorithms provided reliable and visually pleasing results, yet at an increasing computational cost in comparison with simple tractography algorithms. To shorten the computation time, we developed multi‐GPU (graphics processing unit)‐based parallelized versions of deterministic and probabilistic tractography algorithms to investigate their utility for near‐real time tractography. We were able to dramatically reduce the computation time using multiple GPUs (three NVIDIA TESLA C1060s) in comparison to the central processing unit (CPU) sequential processing. Deterministic tractography could accelerate 101 times faster, and probabilistic tractography could accelerate 63 times faster. The results showed that parallel tractography algorithm is well suited with GPU which has fundamentally parallelized architecture. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 256–264, 2013     

An improved fast marching method and its application in Alzheimer's disease

Magnetic resonance diffusion tensor imaging (DTI) provides a noninvasive approach to characterize the fiber pathways in the human brain. Among the fiber tractography algorithms in DTI analysis, the fast marching (FM) method has been widely used in quantitatively analyzing the structural connectivity of the fibers and their changes. However, standard FM only considers the similarity and the principal direction information conveyed by two neighboring voxels. It may have poor tracking performance when image noise and fiber crossing are present. To solve this problem, we introduced an improved FM method employing a memory factor (MFFM) to better characterize the directionality of fiber propagation. Simulation showed that MFFM yields higher tracking accuracy, lower computational load, and better antinoise/crossing performance compared with standard FM. Finally, we applied MFFM to Alzheimer's disease (AD) DTI data to explore the impaired regional connectivity of fiber structure. The results augment the knowledge of the pathological alteration of white matter in AD. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 346–352, 2013     

A geometric flow-based approach for diffusion tensor image segmentation

Diffusion tensor magnetic resonance imaging (DT-MRI, shortened as DTI) produces, from a set of diffusion-weighted magnetic resonance images, tensor-valued images where each voxel is assigned a 3x3 symmetric, positive-definite matrix. This tensor is simply the covariance matrix of a local Gaussian process with zero mean, modelling the average motion of water molecules. We propose a three-dimensional geometric flow-based model to segment the main core of cerebral white matter fibre tracts from DTI. The segmentation is carried out with a front propagation algorithm. The front is a three-dimensional surface that evolves along its normal direction with speed that is proportional to a linear combination of two measures: a similarity measure and a consistency measure. The similarity measure computes the similarity of the diffusion tensors at a voxel and its neighbouring voxels along the normal to the front; the consistency measure is able to speed up the propagation at locations where the evolving front is more consistent with the diffusion tensor field, to remove noise effect to some extent, and thus to improve results. We validate the proposed model and compare it with some other methods using synthetic and human brain DTI data; experimental results indicate that the proposed model improves the accuracy and efficiency in segmentation.     

Estimating intravoxel fiber architecture using constrained compressed sensing combined with multitensor adaptive smoothing

In diffusion magnetic resonance imaging (dMRI), the accuracy of fiber tracking and analysis depends directly on that of intravoxel fiber architecture reconstruction. Several methods have been proposed that estimate intravoxel fiber architecture using low angular resolution acquisitions owing to their shorter acquisition time and relatively low b‐values. But these methods are highly sensitive to noise. We propose an approach to estimating intravoxel fiber architecture in low angular resolution dMRI. The method consists in using a constrained compressed sensing (CCS) method, also known as crossing fiber angular resolution of intravoxel architecture (CFARI) technique, in combination with multitensor adaptive smoothing in which a diffusion‐weighted (DW) image smoothing scheme is constructed according to the properties of the multitensor field estimated using CFARI. The results on synthetic, physical phantom and real brain DW images show that the proposed method is able to better resolve fiber architectures while correctly preserving image edge information, which provides a new tool for investigating the microstructures of biological tissues and for fiber tractography. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 285–296, 2015     

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     

Brightness preserving optimized weighted bi-histogram equalization algorithm and its application to MR brain image segmentation

Medical image segmentation is crucial for neuroscience research and computer-aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast-enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub-histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub-histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.     

一种基于mean shift的多通道图像分割算法

目的针对目标与背景对象的色彩值比较接近的RGB图像中,目标对象难以有效分割的问题,探索一种基于mean shift的RGB多通道图像的分割方法。方法根据RGB图像的3个通道对颜色的敏感性差异,运用均值偏移算法对RGB图像的3个通道分层聚类,再引入可靠性因子,分别对3个单通道的各聚类像素进行可靠性计算,并保留可靠性高的像素作为分割结果,最后采用逻辑"或"运算融合单通道的分割结果,得到最终分割图像。结果与一般分割算法相比,该方法的分割效果好,误分率低,改善了图像的分割质量。结论该算法具有很好的推广性,能够应用于彩色印品缺陷检测、彩色包装图像检测中。     

基于光电缆的分布式温度传感网络的实验研究

本文提出增加一根光纤光栅与光电缆绕制在一起,用于监测电缆中的实时温度.采用有限元分析方法,建立了光电缆温度场模型.使用可调谐脉冲激光为光源,在一根光纤上刻制多个相同中心波长的布拉格光栅,即采用全同光栅作为系统的温度传感器,当光电缆线路中温度发生异常时,反射回来的光栅中心波长发生偏移,通过检测反射光中心波长发生的偏移量可...     

Denoising and segmentation of MR images using fourth order non-linear adaptive PDE and new convergent clustering

At present, digital image processing plays a vital role in medical imaging areas and specifically in magnetic resonance imaging (MRI) of brain images such as axial and coronal sections. This article mainly focused on the MRI brain images. The existing methods such as total variation (MC), parallel MRI, modified pyramidal dual-tree direction filter, adaptive dictionary selection algorithm, classifier methods, and fuzzy clustering techniques are poor in image eminence and precision. Thus, this article presents a novel approach consisting of denoising followed by segmentation. The objective of these proposed methods was visual eminence improvement of medical images to examine tumor extent using an adaptive partial differential equation (APDE)-based analysis with soft threshold function in denoising. The fourth order, nonlinear APDE was used to denoise the image depending on gradient and Laplacian operators associated with the new adaptive Haar-type wavelet transform. A second approach was the new convergent K-means clustering for segmentation. The convergent K-means procedure diminishes the summation of the squared deviations of structures in a cluster from the center. The significance of these proposed methods was to compute their performances in terms of mean squared error, peak signal-to-noise ratio, structure similarity, segmentation accuracy, false hit, missed-term, and elapsed time. The results were analyzed with the MATLAB software.     

A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique

This proposed work is aimed to develop a rapid automatic method to detect the brain tumor from T2‐weighted MRI brain images using the principle of modified minimum error thresholding (MET) method. Initially, modified MET method is applied to produce well segmented and sub‐structural clarity for MRI brain images. Further, using FCM clustering the appearance of tumor area is refined. The obtained results are compared with corresponding ground truth images. The quantitative measures of results were compared with the results of those conventional methods using the metrics predictive accuracy (PA), dice coefficient (DC), and processing time. The PA and DC values of the proposed method attained maximum value and processing time is minimum while compared to conventional FCM and k‐means clustering techniques. This proposed method is more efficient and faster than the existing segmentation methods in detecting the tumor region from T2‐weighted MRI brain images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 77–85, 2015     

ANFIS‐EM approach for PET brain image reconstruction

In this article, for the reconstruction of the positron emission tomography (PET) images, an iterative MAP algorithm was instigated with its adaptive neurofuzzy inference system based image segmentation techniques which we call adaptive neurofuzzy inference system based expectation maximization algorithm (ANFIS‐EM). This expectation maximization (EM) algorithm provides better image quality when compared with other traditional methodologies. The efficient result can be obtained using ANFIS‐EM algorithm. Unlike any usual EM algorithm, the predicted method that we call ANFIS‐EM minimizes the EM objective function using maximum a posteriori (MAP) method. In proposed method, the ANFIS‐EM algorithm was instigated by neural network based segmentation process in the image reconstruction. By the image quality parameter of PSNR value, the adaptive neurofuzzy based MAP algorithm and de‐noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package. Thus ANFIS‐EM algorithm provides 40% better peak signal to noise ratio (PSNR) when compared with MAP algorithm. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 1–6, 2015     

天津大学光纤传感技术研究部分最新进展

本文介绍了天津大学在光纤传感技术研究领域的最新进展.主要为:基于白光干涉实现了非本征光纤法珀和FBG并行解调,法珀腔长测量误差0.81 μm,FBG波长测量误差14 pm;基于光纤有源内腔结构夹现了乙炔气体传感,灵敏度优于100ppm;基于保偏光纤实现了分布式传感,灵敏度可达6 cm;基于边缘滤波器开发了光纤光栅解调仪,波长分辨力可达1.2pm,扫描速率超过200kHz;采用全光纤OCT技术实现了牙齿模型的二维、三维扫描;实现了光纤陀螺光纤环的温度、振动等动态特性检测.     

基于F-P标准具和Music算法的FBG动态应变传感系统

提出了基于可调光纤环形激光器结构,并采用F-P标准具解调的光纤布拉格光栅(FBG)动态应变传感系统,具有高达60 dB的光学信噪比.F-P标准具用来作为一个边缘滤波器探测FBG波长的漂移,这样的解调方式具有稳定性高的优点.为了提高动态应变测量系统的分辨率,采用了Music算法进行频谱分析.实验结果显示,在700Hz时的动态应变分辨率达到了0.1με/Hz~(1/2),是传统FFT算法的10倍.     

MRI brain image classification—a hybrid approach

The present article proposes a novel computer‐aided diagnosis (CAD) technique for the classification of the magnetic resonance brain images. The current method adopt color converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on IGSFFS (Information gain and Sequential Forward Floating Search) and Multi‐Class Support Vector Machine (MC‐SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The proposed hybrid evolutionary segmentation algorithm which is the combination of WFF(weighted firefly) and K‐means algorithm called WFF‐K‐means and modified cuckoo search (MCS) and K‐means algorithm called MCS‐K‐means, which can find better cluster partition in brain tumor datasets and also overcome local optima problems in K‐means clustering algorithm. The experimental results show that the performance of the proposed algorithm is better than other algorithms such as PSO‐K‐means, color converted K‐means, FCM and other traditional approaches. The multiple feature set comprises color, texture and shape features derived from the segmented image. These features are then fed into a MC‐SVM classifier with hybrid feature selection algorithm, trained with data labeled by experts, enabling the detection of brain images at high accuracy levels. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. The proposed method provides highest classification accuracy of greater than 98% with high sensitivity and specificity rates of greater than 95% for the proposed diagnostic model and this shows the promise of the approach. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 226–244, 2015     

Fiber and interface strength distribution studies with the single-fiber composite test

Application of the single-fiber composite (sfc) tension test for fiber and interface strength determination is discussed. Fiber breaking and fiber/matrix debond propagation are modelled by Poisson processes. Fiber fragment length distribution as well as debond length dependency upon the applied stress are derived and their interrelation revealed.

Acoustic emission monitoring of the sfc during a test is utilized to obtain the dependency of mean fragment length upon stress and consequently on the Weibull distribution shape and scale parameters. Excellent agreement with data obtained by notoriously complicated conventional fiber tests is observed.     

一种新型全光纤弹速测量系统的研制

研制了一种新型的利用激光光束反射原理的全光纤弹速测量系统。该系统采用了全光纤结构和光纤耦合器等无源器件,以输出光功率1mW, 工作波长1300nm的半导体激光器作为测试系统的光源,用光纤耦合器进行分光,实现了在一根光纤中同时传输光源和接受目标反射的信号光,避免了复杂的调节和准直过程。该系统结构简单、可靠性高,利用它,成功地测量了霍普金森杆发射的子弹速度,结果表明其速度测量相对不确定度小于1%。     

一种改进的势函数聚类多阈值图像分割算法

针对基于势函数聚类的多阈值图像分割算法的不足,定义了伪势的概念,并在原算法基础上提出了一种改进的图像分割算法。由伪势概念确定了伪势合并的判别方法,按照此方法,当相邻的两个峰之间的距离小于所定义的自适应模糊伪势因子时,则应该进行伪势合并。改进后的算法在计算剩余势函数时判断是否存在伪势,然后在势划分函数组的确定过程中相应地进行伪势合并计算。利用多幅图像进行了多阈值分割的仿真试验,结果表明,改进的基于势函数的多阈值图像分割算法具有更好的鲁棒性和分割效果。     

A comprehensive review: Segmentation of MRI images—brain tumor

Segmentation of tumors in human brain aims to classify different abnormal tissues (necrotic core, edema, active cells) from normal tissues (cerebrospinal fluid, gray matter, white matter) of the brain. In existence, detection of abnormal tissues is easy for studying brain tumor, but reproducibility, characterization of abnormalities and accuracy are complicated in the process of segmentation. The magnetic resonance imaging (MRI)‐based segmentation of tumors in brain images is more enhancing and attracting in current years of research studies. It is due to non‐invasive examination and good contrast prone to soft tissues of images obtained from MRI modality. Medical approval of different segmentation techniques depends on the benchmark and simplicity of the method. This article incorporates both fully‐automatic and semi‐automatic methods for segmentation. The outlook study of this article is to provide the summary of most significant segmentation methods of tumors in brain using MRI. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 295–304, 2016     

光纤通信系统中器件的总剂量效应研究进展

光纤通信系统的诸多优点,使其可能在空间得到广泛应用。着重介绍了光纤通信系统的基本组成、特点及总剂量效应研究方面的进展。