X射线康普顿背散射安检图像混合滤波器设计

针对X射线康普顿背散射安检图像在噪声、背景均匀性和对比度等方面存在的问题,根据辐射成像系统自身的特点设计了一种混合滤波器,用于改善图像质量。经过图像滤波后,在监视器终端获得了层次清晰、轮廓分明、背景均匀的X光背散射图像。     

松质骨组织的若干超声参量成像方法

介绍了两种松质骨组织超声参量成像方法,即宽带超声衰减成像和超声传导速度成像,概述了这两种基于透射法的成像方法的基本原理以及应用情况．并指出了采用超声透射法参量成像时存在的问题。然后对基于背散射法的超声背散射系数成像方法的可行性进行了分析和讨论,最后对进一步的研究工作进行了展望。     

THE APPLICATIN OF MSSBAUER EFFECT IN MEASUREMENT OF RETAINED AUSTENITE IN HIGH SPEED STEEL

本文首先应用透射Mssbauer 法测定了一组标准样品中奥氏体的含量,并且从实验上得到了与已知含量相吻合的结果。然后用背散射X 射线M(?)ssbauer 法和X 射线衍射法测量了一组不同热处理制度下M_2高速钢中的残留奥氏体含量,两种方法的测量结果可相互比较.     

Mssbauer 效应在高速钢中残留奥氏体测定上的应用

本文首先应用透射 Mssbauer 法测定了一组标准样品中奥氏体的含量,并且从实验上得到了与已知含量相吻合的结果。然后用背散射 X 射线 M(?)ssbauer 法和 X 射线衍射法测量了一组不同热处理制度下 M_2高速钢中的残留奥氏体含量,两种方法的测量结果可相互比较.     

一种基于人眼视觉特性的偏振图像融合方法

根据偏振参量图像之间存在的信息冗余性和互补性,提出了一种基于人眼视觉特性的偏振图像融合方法.该方法首先对线偏振度图像和偏振角图像进行基于能量特征的融合,得到偏振特征图像;然后,利用五株采样提升小波变换实现偏振特征图像和合成强度图像的融合;最后,根据人眼对局部对比度敏感的特点,采用偏振度图像对融合结果进行对比度增强处理.实验结果表明,融合后的图像不仅突出了场景的偏振信息,而且包含了丰富的强度信息,目标与背景的对比度也得到了显著增强.     

基于分形特征和导引滤波的可见光与红外图像融合算法

为充分利用红外图像的目标指示信息,本文提出了一种基于分形特征和导引滤波的可见光与红外图像融合算法。该算法首先采用分形特征对红外图像中的人造目标进行增强,通过阈值分割得到目标分布图。待融合图像经过一层分解得到近似图像和细节图像,基于目标分布图利用导引滤波分别得到可见光与红外近似图像与细节图像的融合系数。实验结果表明,融合后图像充分结合了可见光图像的背景信息与红外图像中的目标信息,有利于后续的目标识别任务。     

非亚采样Contourlet遥感图像融合

非亚采样 Contourlet 变换是在非亚采样塔型滤波器及非亚采样方向滤波器组的基础上建立起来的,它是一种移不变多方向多尺度图像表示方法.介绍了该变换的结构特点与系数分布特性,并研究了基于非亚采样Contourlet 变换的图像融合算法.该算法利用非亚采样Contourlet 的平移不变性以及NSCT 系数特点,有效准确地提取图像边缘与细节区域,并分别在高、低频域针对不同区域采用不同的融合方法,有效挖掘了待融合图像中的有效信息.这种具有多分辨率分析和多方向滤波特点的处理方法,提高了融合后遥感图像中的信息量和清晰度,对不同分辨率不同方向上的信息进行挖掘及融合,解决了传统小波融合算法中方向数目受限的不足.通过仿真实验与传统融合方法进行比较,验证了该方法的有效性和优越性.     

基于导向滤波与分形维度的图像加权融合算法

目的为了解决当前图像融合技术中易丢失图像信息,不能较好地保持源图像的边缘与纹理信息,从而降低了图像分辨率与视觉质量,使其不能对目标进行清晰、完整、准确地信息描述等问题。方法提出一种导向滤波耦合分形维度的图像加权融合方案。首先对源图像进行预处理,通过增强对比度来提高图像的动态范围。通过小波变换将图像分解为低频与高频部分,并引入导向滤波器,对其低频、高频成分进行处理,获取相应的低频、高频权重,较好地保持图像的边缘信息。然后,通过提取局部特征分形维数来获取微小纹理特征。最后,定义一种加权融合方案,根据低频与高频权重进行融合,得到最后融合图像。结果实验数据表明,与当前常用图像融合算法比较,文中算法具有更好的融合视觉效果,更好地保持了源图像的真实信息;在信息熵、交互信息、平均梯度和标准差等4种定量分析指标方面,所提算法具有更大的优势。结论所提算法具有良好的融合质量,在图像处理领域具有一定的参考价值。     

基于小波变换的自适应图像融合算法

针对现有图像融合方法存在的光谱信息和空间细节信息不能较好兼顾的问题,建立了图像内容自适应的融合准则和一致性选取准则,提出了一种基于小波变换的自适应图像融合算法,实现了多光谱图像与全色图像的融合,并对融合图像进行了主、客观评价。着重从图像融合如何提高目标的区分度和识别率的角度给出主观评价,通过光谱扭曲度、清晰度客观分析多光谱与全色图像的融合效果。实验结果表明,该算法充分利用了全色图像的空间细节特征、图像边缘和方向性特征信息,保留了多光谱图像的光谱信息特征,提高了融合图像的主观效果,有利于信息的提取和目标解译。在光谱和空间细节综合保持方面,该算法优于IHS融合方法和传统的二进小波融合方法。     

信息融合的飞机识别方法

以信息融合的理论为基础，利用从可见光图像序列和8-12μm长波红外图像序列中提取的信息对不同种类飞机进行识别。采用矩特征并结合BP神经网络的方法，分别在特征级和决策级两个不同层次上实现了信息融合。实验结果表明，通过信息融合进行飞机识别的准确率可达到90％以上。     

应用统计信号处理和模糊数学的图像融合算法

针对多传感器图像在像素级上的融合问题,将模糊数学理论引入到图像融合模型。该模型假定理想的融合后的图像包含场景所有的信息;将它乘上一个模糊因子,再加上随机噪声,可用来描述某一个成像传感器中获得的场景图像;不同的传感器对应不同的模糊因子和噪声。在此基础上,提出了建立在非多尺度分解框架下的图像融合算法。它以各传感器获取的图像作为输入条件,应用统计信号处理中的EM算法,求出针对不同传感器的噪声参数和模糊因子,通过迭代估计出融合的图像。实验结果显示,该算法获得的融合图像的互信息和联合熵分别达到3．5079和24．732,均优于加权平均融合法、小波融合算法和Laplacian融合算法的融合质量。     

Multimodal sensor medical image fusion based on mutual‐structure for joint filtering using sparse representation

Multimodal sensor medical image fusion has been widely reported in recent years, but the fused image by the existing methods introduces low contrast information and little detail information. To overcome this problem, the new image fusion method is proposed based on mutual‐structure for joint filtering and sparse representation in this article. First, the source image is decomposed into a series of detail images and coarse images by mutual‐structure for joint filtering. Second, sparse representation is adopted to fuse coarse images and then local contrast is applied for fusing detail images. Finally, the fused image is reconstructed by the addition of the fused coarse images and the fused detail images. By experimental results, the proposed method shows the best performance on preserving detail information and contrast information in the views of subjective and objective evaluations.     

一种基于Directionlet变换的图像融合算法

为了提高图像融合效果,提出了一种基于Directionlet变换的图像融合算法.首先对已配准的待融合源图像由给定的生成矩阵分别进行陪集分解,得到每个陪集对应的子图;接着将每两个子图相减,得到源图像的高频和低频分量,其中边缘、纹理等奇异特征包含在高频分量中;然后对低频分量采用直接平均融合的方法进行系数选择,对高频分量选择子区域边缘信息较强的系数;最后,通过Directionlet陪集分解的反变换,得到融合后的图像.多聚焦图像融合实验表明,在主观视觉上,该算法明显更好地融合了边缘等图像特征,从而较好地保持了左右聚焦图像各自的细节信息;在客观评价上,通过熵、平均梯度、标准差和互信息量等性能参数比较,该方法也优于小波变换和其他的融合方法.     

Modified Global Flower Pollination Algorithm‐based image fusion for medical diagnosis using computed tomography and magnetic resonance imaging

Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.     

Survey and analysis of various image fusion techniques for clinical CT and MRI images

The research and development of biomedical imaging techniques requires more number of image data from medical image acquisition devices, like computed tomography (CT), magnetic resonance imaging (MRI), positron emission technology, and single photon emission computed tomography. Multimodal image fusion is the process of combining information from various images to get the maximum amount of content captured by a single image acquisition device at different angles and different times or stages. This article analyses and compares the performance of different existing image fusion techniques for the clinical images in the medical field. The fusion techniques compared are simple or pixel‐based fusion, pyramid‐based fusion, and transform‐based fusion techniques. Four set of CT and MRI images are used for the above fusion techniques. The performance of the fused results is measured with seven parameters. The experimental results show that out of seven parameters the values of four parameters, such as average difference, mean difference, root mean square error, and standard deviation are minimum and the values of remaining three parameters, such as peak signal to noise ratio, entropy, and mutual information are maximum. From the experimental results, it is clear that out of 14 fusion techniques taken for survey, image fusion using dual tree complex wavelet transform gives better fusion result for the clinical CT and MRI images. Advantages and limitations of all the techniques are discussed with their experimental results and their relevance. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 193–202, 2014.     

Robust medical image registration based on phase congruency and regional mutual information

Abstract

In this paper, a new approach of multi-modality image registration is represented with not only image intensity, but also features describing image structure. There are two novelties in the proposed method. First, instead of standard mutual information based on joint intensity histogram, a graph-based implementation of multi-dimensional regional mutual information is employed, which allows neighbourhood information to be taken into account. Second, a new feature image is obtained by means of phase congruency, which is invariant to brightness or contrast changes. By incorporating these features and intensity into regional mutual information, we can combine aspects of both structural and neighbourhood information together, which offers a more robust and a high level of registration accuracy that is essential in application to the medical domain.     

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.     

基于多源信息的多参数电磁层析成像方法研究

电磁层析成像(EMT)是电学层析成像技术中的研究热点,但现有EMT方法主要通过单一检测信号对电导率或磁导率分布单独成像。提出了一种基于多源信息的多参数EMT方法。仿真实验结果表明,基于磁感应强度和互感信息的EMT可以分别实现电导率和磁导率分布成像。通过计算重建图像的均方根误差和相关系数,发现线圈和磁阻传感器个数从8个增加到12个时,重建图像的质量有所提高。利用阈值分割和RGB模型,实现电导率和磁导率分布的融合成像。利用融合图像的互补特性,可以改善重建图像质量,并实现了气-液-固三相流型分布的可视化,证明了基于多源信息的多参数EMT方法的可行性。     

SAR图像配准的二元Rayleigh分布互信息方法

合成孔径雷达(Synthetic Aperture Radar,简称SAR)图像中乘性斑点噪声的存在,使得传统的互信息配准方法中的插值假象更为严重.本文把单视SAR幅值图像同质区域服从Rayleigh分布这一先验信息引入互信息配准方法中,对同源单视SAR幅值图像进行配准.以合成图像和真实图像的试验结果和分析证实了方法的...