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。     

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.     

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。     

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.     

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.

     

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...     

Multichannel image registration by feature-based information fusion

This paper proposes a novel nonrigid inter-subject multichannel image registration method which combines information from different modalities/channels to produce a unified joint registration. Multichannel images are created using co-registered multimodality images of the same subject to utilize information across modalities comprehensively. Contrary to the existing methods which combine the information at the image/intensity level, the proposed method uses feature-level information fusion method to spatio-adaptively combine the complementary information from different modalities that characterize different tissue types, through Gabor wavelets transformation and Independent Component Analysis (ICA), to produce a robust inter-subject registration. Experiments on both simulated and real multichannel images illustrate the applicability and robustness of the proposed registration method that combines information across modalities. This inter-subject registration is expected to pave the way for subsequent unified population-based multichannel studies.     

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。     

Adaptive predictive multiplicative autoregressive model for medical image compression

In this paper, an adaptive predictive multiplicative autoregressive (APMAR) method is proposed for lossless medical image coding. The adaptive predictor is used for improving the prediction accuracy of encoded image blocks in our proposed method. Each block is first adaptively predicted by one of the seven predictors of the JPEG lossless mode and a local mean predictor. It is clear that the prediction accuracy of an adaptive predictor is better than that of a fixed predictor. Then the residual values are processed by the MAR model with Huffman coding. Comparisons with other methods [MAR, SMAR, adaptive JPEG (AJPEG)] on a series of test images show that our method is suitable for reversible medical image compression.     

薄云薄雾影响下目标影像自适应增强

卫星图像在拍摄过程中,由于云雾等因素影响,导致影像的亮度增加,目标影像对比度和分辨率下降。针对薄云薄雾对卫星图像的影响,提出目标影像的自适应增强算法。实验表明,该算法具有较强的自适应性。提高了薄云薄雾影响下的目标影像的对比度,恢复了该区域的亮度,同时,保持了薄云薄雾影响弱的区域的对比度和亮度,达到了目标影像自适应增强的目的。     

基于对偶树复小波域HMT模型的遥感图像融合

针对遥感图像融合领域的实际应用,提出一种基于对偶树复小波变换与隐马尔可夫树模型结合的图像融合新方法。该算法将分别具有高光谱和高空间分辨率优势的两幅图像进行复小波变换,再对分解后不同频率域的系数选择不同的融合规则处理。采用低频系数加权平均;高频系数先建模,再基于区域能量规则处理的方法,最后完成逆变换得到重构图像。将该算法与其他几种图像融合方法进行比较,实验表明,该算法能够取得较为理想的效果。     

Gradient-based multiresolution image fusion

A novel approach to multiresolution signal-level image fusion is presented for accurately transferring visual information from any number of input image signals, into a single fused image without loss of information or the introduction of distortion. The proposed system uses a "fuse-then-decompose" technique realized through a novel, fusion/decomposition system architecture. In particular, information fusion is performed on a multiresolution gradient map representation domain of image signal information. At each resolution, input images are represented as gradient maps and combined to produce new, fused gradient maps. Fused gradient map signals are processed, using gradient filters derived from high-pass quadrature mirror filters to yield a fused multiresolution pyramid representation. The fused output image is obtained by applying, on the fused pyramid, a reconstruction process that is analogous to that of conventional discrete wavelet transform. This new gradient fusion significantly reduces the amount of distortion artefacts and the loss of contrast information usually observed in fused images obtained from conventional multiresolution fusion schemes. This is because fusion in the gradient map domain significantly improves the reliability of the feature selection and information fusion processes. Fusion performance is evaluated through informal visual inspection and subjective psychometric preference tests, as well as objective fusion performance measurements. Results clearly demonstrate the superiority of this new approach when compared to conventional fusion systems.     

Multimodal biometric image watermarking using two-stage integrity verification

In this paper, we propose multimodal biometric image watermarking scheme through a two-stage integrity verification method using the hidden thumbnail feature vectors for safe authentication of multimodal biometrics data, face and fingerprint, respectively. It is basically blind and spread spectrum-based robust watermarking method. The proposed method enables us to detect a tampered region by controlling watermark embedding strength to meet the requirement of predefined watermark extraction threshold. The key idea is that the thumbnail feature vectors of a face image as a watermark pattern are utilized by embedding into a fingerprint image in order to verify the integrity of respective biometric data. The first stage of integrity verification for a fingerprint image is done by deciding the validity of extracted thumbnail patterns. The second stage of integrity verification for a face image is done by one-to-one matching between the thumbnail feature vectors extracted from a face image and the thumbnail one of the received face image. Experimental results showed that the proposed method has a high detection rate of the forged biometric data and guarantees the security assurance.     

多传感器图像融合技术

信息融合技术作为一门新兴学科,近年来已取得突飞猛进的发展,已在军事和民事方面获得了广泛的应用,对人类的生产生活方式产生了重大的影响。主要介绍了多传感器图像融合的3个融合层次及在每个融合层次典型的融合方法。单一的融合方法有各自的优势和不足,为了克服单一融合方法存在的问题,可以将单一的融合方法进行结合,通过从灰度标准差、平均灰度梯度和熵等方面对融合后图像效果比较可以发现,将两种单一的融合方法结合,可以很好地实现优势互补,获得更理想的融合效果并对多种不同的融合方法进行分析总结,可以为融合算法的应用提供一定的参考。     

改进多尺度融合结合小波域HMT模型的遥感图像分割

提出了一种结合权值背景融合的小波域多尺度图像分割方法。首先通过小波域隐马尔可夫树模型获得图像各个尺度上的初始分割,然后为各个尺度上每一分割像素点分别赋予权值,并建立一种融合父子尺度信息的新背景模型,最后利用权值背景融合各个尺度图像初始分割结果,得到像素级分割。仿真结果表明,该方法可得到优于已有文献的分割效果。     

基于小波和小波包变换的医学图像融合比较

分别用小波变换和小波包变换对CT和PET图像进行融合,用常用的十种图像融合质量评价指标对融合效果进行比较.结果表明小波包变换的融合质量评价指标只有部分是优于小波变换的.在对CT和PET图像进行融合时,小波包并不一定更占优势,要根据自己的实际需求来决定选择使用小波变换还是小波包变换.