基于小波变换的多模态医学图像的融合方法

针对单一模态的图像不能提供足够的信息,可以将互补的图像进行融合。提出一种基于小波变换的医学图像融合方法。首先对待融合的医学图像进行小波变换,分别得到低频和高频分量。对于低频分量,采用基于局部区域能量的方法进行融合;对于高频分量,采用基于局部区域系数之差的加权和方法进行融合;最后通过小波逆变换得到融合图像。实验结果表明,相比同类研究方法,该算法在保证图像质量的同时可增强图像的空间细节表现能力。     

基于NSST和高斯混合模型的医学图像融合算法

提出了一种基于非下采样剪切波变换（Non-subsampled shearlet transform, NSST）和高斯混合模型的医学彩色图像融合算法。首先将彩色图像转换到HSI颜色空间,提取其色度分量图像、饱和度分量图像和强度分量图像;然后对强度分量图像和MRI图像进行NSST变换,其中低频系数采用基于区域系数改进拉普拉斯能量和（Sum-modified-Laplacian,SML）加权的融合规则,高频系数采用高斯混合模型估计参数取大的融合规则;对融合后的系数进行NSST逆变换重构融合后的强度分量图像;最后将融合后的强度分量图像与色度分量图像、饱和度分量图像混合得到融合后的HSI图像,再将HSI图像转换到RGB颜色空间得到融合后的彩色图像。仿真实验表明,该算法在视觉效果和客观评价指标上具有更好的融合效果。     

Multimodal medical image fusion using non-subsampled shearlet transform and pulse coupled neural network incorporated with morphological gradient

This research proposes a novel fusion scheme for non-subsampled shearlet transform (NSST) which is based on simplified model of pulse coupled neural network (PCNN). The images to be fused are acquired from Postgraduate Institute of Medical Education and Research, Chandigarh, India, and internet repository. The image database contains computed tomography and T2-weighted magnetic resonance images. The images to be fused are decomposed into approximation and detail sub-bands using NSST. The regional energy-based activity measure with consistency verification is applied to fuse the approximation sub-band of NSST. The novel morphological gradient of detail sub-bands is fed as external stimulus to PCNN to fuse detail sub-bands. The proposed method is compared with five state-of-the-art fusion schemes visually and using five fusion performance parameters. It is observed that the resultant images of the proposed fusion scheme show appropriate fusion characteristics and retain the bone, CSF and edema details in the clinical format required for disease evaluation by the radiologists. The proposed scheme requires lesser computational time than other state-of-the-art PCNN-based fusion schemes.     

云平台网络多重纹理图像匹配融合方法研究

传统的图像匹配融合方法在匹配多重纹理图像时,很容易出现误差匹配,融合后的图像清晰度不高,轮廓不鲜明,针对上述问题,在云平台网络上研究了一种新的多重纹理图像匹配融合方法。首先,计算多重纹理图像的匹配代价,分析图像像素的相似度和特异性,构建动态规划路径,在不同网络结构下匹配多重纹理图像;然后,建立树状图对图像进行融合;最后,利用视察矫正方法将匹配融合得到的误差点消除。为验证该方法的工作效果,与传统匹配融合方法进行实验对比,结果表明,给出的方法能够清晰地得到像素点云,使融合后的图像轮廓鲜明,画质清晰,适用于图像重构。     

基于Contourlet变换的医学图像融合研究

介绍了对医学图像进行多分辨率、多方向分解的Contourlet变换。针对医学图像的特点,提出一种改进的中心区域能量加权平均融合规则。进行了不同Contourlet分解层次以及基于Con tourlet变换的不同融合规则的医学图像融合的仿真实验。结果表明,图像的质量有明显提高。     

Perceptual quality assessment for multimodal medical image fusion

Recent years have witnessed that the multimodal medical image fusion (MMIF) plays critical roles in clinical diagnostics and treatment. Many MMIF algorithms have been proposed to improve the MMIF images quality. The quality of multimodal medical fused images will significantly affect the results of the clinical diagnosis. However, little work has been designed to evaluate the effectiveness of MMIF algorithms and the quality of MMIF images. To this end, this paper presents a perceptual quality assessment method for MMIF. A MMIF image database (MMIFID) is first built to employ the classical MMIF algorithms, and the subjective experiment is conducted to assess the quality of each fused image. Then, a no-reference objective method is proposed for the perceptual quality evaluation of MMIF images,which uses Pulse Coupled Neural Network (PCNN) in Non-subsampled Contourlet Transform (NSCT). A fused image is decomposed by NSCT into low frequency sub-band (LFS) and high frequency sub-band (HFS). It is used to motivate the PCNN processing, and large firing times are employed to measure LFS and HFS. Finally, two components evaluation results are combined to obtain the overall objective quality score. Experimental results based on the MMIFID indicate that our presented method outperforms the existing image fusion quality evaluation metrics, and it provides a satisfactory correlation with subjective scores, which shows effectiveness in the quality assessment of medical fused images.     

基于Minkowski距离最小化的多模态图像配准

本文基于R.Gan等人提出的图像配准方法,利用变量间的Minkowski不等式关系理论,引入全新的联合灰度分布间Minkowski距离(Minkowski Distance,MD),以此距离作为相似度进行多模态图像配准.经分析,因Minkowski距离的引入,使得本文的优化目标函数可保持良好的全局凸性和光滑性,优化求解可以有效地克服局部极值问题,配准成功率较R.Gan等人的配准方法有显著提高.大量实验表明,本文方法的配准精度可以与经典的基于互信息量的配准方法一比高低.     

压缩感知及其在医学图像融合中的应用

压缩感知在采样的同时完成信号的压缩,为解决医学图像融合过程中时间复杂度高、传输数据量大的问题提供了新方法.本文围绕压缩感知在医学图像像素级融合做了5个方面的工作:第一,给出了基于压缩感知的医学图像融合框架;第二,讨论了基于贝叶斯、贪婪迭代、凸松弛等四类重构算法;第三,梳理出医学图像像素级融合的七类方法;第四总结出基于压缩感知的四种医学图像融合路径;第五,指出了目前研究的难点和应用前景.     

基于小波融合技术的医学图像增强方法

为了改善医学图像质量,在分析小波变换原理的基础上,根据医学图像的成像特征,提出了一种基于小波融合技术的医学图像增强方法.首先,对待增强图像进行多层小波变换处理,获取各个频率的小波系数.然后分别对各频率系数进行相应的处理后,进行小波重构并进行对比度增强处理,获得增强图像1;同时对待增强图像进行对数变换和对比度拉伸处理,获得增强图像2.最后将增强图像1和增强图像2变换至小波域进行图像融合处理,以获得最终的增强图像.结果表明:本文提出的增强方法具有明显的增强效果.该增强方法能有效提高医学图像的对比度,增强边缘细节信息,突出病灶点的位置,达到较好的增强效果,为医疗工作者观察病症提供更加清晰准确的依据.     

Adaptive region-based image fusion using energy evaluation model for fusion decision

A new adaptive region-based image fusion approach is proposed. To implement image segmentation, the piecewise smooth Mumford-Shah segmentation algorithm is studied and a fast and simple method is proposed to solve the energy function. Two complementary functions u ⁺ and u ⁻ of the algorithm, which are respectively looked as objects and background of the image, are extended into the whole image domain, and they are computed by linear or nonlinear diffusion. The key to the algorithm is to make optimal fusion decisions for every segmented region. For this purpose, an evaluation approach has to be given to measure the performances of the available fusion rules. Therefore an energy-based evaluation model, derived from the Total Variation principle, is proposed. By numerical experiment it has been demonstrated that despite an increase in complexity, the new approach has shown a number of advantages over previous ones, for example the ability to preserve all relevant information and remove some of side effects such as reducing contrast and sensitive to error of registration.     

基于隶属云理论的主观信任评估模型研究

根据主观信任的主观性、模糊性和随机性等特点,引入隶属云模型理论对信任进行建模,给出了信任云和信任等级云的定量描述,提出了信任评价云的计算、合成、综合运算和主体信任等级的评估.模拟实验结果表明该信任评估模型是有效的.     

改进的ROMP算法及其在医学图像融合中的应用

针对正则化正交匹配追踪(ROMP)算法在压缩感知(C S)重构中需预估稀疏度导致重构精度不稳定的问题,提出一种改进的 ROMP算法。由于观测信号能够继承原始信号特征,在选择候选集原子过程中引入自适应弱 选择标准,依 据观测信号的信息量设定弱选择标准,实现稀疏度自适应调整。将改进ROMA的算法应用于CS 框架下的医学图 像融合,并提出一种结合观测信号结构相似度的融合规则,当待融合的观测信号之间结构相 似度较高时, 说明待融合的原始信号之间同样具有相似性,以两者信息量的加权作为融合规则。同理,当 待融合的观测 信号结构相似度较低时,选择信息量较大的观测信号作为融合后的观测信号。实验结果表明 ,改进ROMP 算法的重构图像质量优于OMP、ROMP、SAMP等算法,其峰值信噪比(PSNR )提高了2~3dB。应用于医学图像 融合时,得到融合图像具有较好的人类视觉特效,保留了源图像中大部分特征信息,可在较 短时间内得到优质的融合结果。     

一种新型异源图像融合质量评价模型

提出了一种基于FNN的异源图像融合质量评价模型。该模型将融合图像的主观评价结论样本集作为模糊期望输出,并利用高斯隶属度函数将多种典型图像融合客观评价指标进行模糊化,作为网络输入样本。通过网络学习,生成评价指标权重与隶属度函数的相关参数,并采用动量因子提高了网络的学习效率。实验结果表明,采用该方法进行异源图像融合质量评价,评价结论符合人眼的观察特性,主、客观评价结论具有较好的一致率,为融合图像自动化评价的实现提供了有效的途径。     

A novel approach using structure tensor for medical image fusion

Medical image fusion has been shown to be effective in supporting clinicians make better clinical diagnoses. Although many algorithms have been proposed for synthesis, they still have certain limitations. Some limitations can be seen as the synthesized image is reduced in contrast or details are not preserved. In this paper, we propose an image fusion algorithm to solve the problems mentioned above. Firstly, an image decomposition method is proposed to decompose the image into two components. This method is based on the Gaussian filter and the Weighted mean curvature filter. Secondly, a fusion method for high-frequency components is based on local energy function using Structure tensor saliency. Finally, we create an adaptive fusion rule using the Marine Predators Algorithm optimization method to fuse low-frequency components. Five latest algorithms and five evaluation indexes have been used to test the proposed algorithm’s effectiveness. The obtained experimental results show that the composite image is significantly improved in quality as well as well preserved the information from the input image.

     

基于云模型相似度的图像分类方法研究

云模型相似度方法是对象相似性分析的一种重要方法.为提高图像分类的准确性,提出一种基于云模型相似度的图像分类方法.首先给出图像云模型的定义,然后根据云模型方法的逆向云算法对图像云模型特征进行数字特征计算,最后引入云模型相似性测度方法对图像云模型相似性进行测算并确定图像分类.仿真结果表明,文章所提方法可准确地对图像进行分类,且计算效率较高.     

融合知识的中文医疗实体识别模型

从医疗文本中抽取知识对构建医疗辅助诊断系统等应用具有重要意义.实体识别是其中的核心步骤.现有的实体识别模型大都是基于标注数据的深度学习模型,非常依赖高质量大规模的标注数据.为了充分利用已有的医疗领域词典和预训练语言模型,本文提出了融合知识的中文医疗实体识别模型.一方面基于领域词典提取领域知识,另一方面,引入预训练语言模...     

一种基于云模型的改进型混洗蛙跳算法

针对经典混洗蛙跳算法在函数优化中易陷入局部最优和早熟收敛等缺点,结合云模型在定性与定量之间相互转换的优良特性,提出一种基于云模型的改进型混洗蛙跳算法。其思想是通过反向学习机制初始化种群,再通过正态云算子求解全局最优个体和子群最优个体周围的更优值,最后利用混沌理论对个别个体进行变异来跳出局部最优解。典型复杂函数测试表明,该算法能有效找出全局最优解,特别适宜于多峰值函数寻优。     

基于云模型的选煤厂CIMS环境下数据融合

结合数据融合综合处理信息的特点和云模型定性与定量相结合的信息处理方法,分析了选煤厂计算机现代集成制造系统(CIMS)环境的特点,提出了选煤厂CIMS环境下数据融合的体系结构,详细分析了3个分系统基于云模型的数据融合过程,创建了各个分系统云模型数据融合的模型图,同时提出了各个分系统的数据融合采用的方法及其云模型算法。通过算法仿真实验表明选煤厂采用云模型数据融合的方法对其控制生产过程有较大改进。     

融合纹理特征和形状特征的医学图像分类方法

针对近些年来,女性人群中乳腺恶性肿瘤患者逐年增长的趋势,提出了一种融合纹理特征和形状特征的乳腺超声肿瘤图像的识别方法。该方法不但结合了临床医生的诊断经验,而且有效利用了数字图像特征提取技术,提取出的特征能反映出良性肿瘤和恶性肿瘤的本质区别,将样本进行特征提取并通过支持向量机(SVM)技术分类,该方法取得了良好的分类效果。实验结果证明文中的特征融合方法十分有效。