MRI图像的小波融合处理

背景:小波图像融合是将两幅图像融合在一起,以获取对同一场景的更为精确、全面、可靠的图像描述.目的:用小波变换图像融合技术融合MRI脑梗死图像,以恢复缺损图像.方法:图像融合的主要机制是利用二维小波分析法对MRI脑梗死图像进行小波分解,并对高低频信号采用多种融合方式进行融合.通过对比不同融合方式后的效果图,找出最适合本部位MRI图像的融合方法.结果与结论:不同方式的融合技术能成功修复不同的缺损部位,多种融合方式的合适组合能完全修复多处缺失部位.对于文中给出的MRI脑梗死图像,采用最小值融合方式的融合效果最好.提示使用二维小波分析法处理医学图像,简便快捷,能有效改善图像的视觉效果,辅助临床诊断.     

比较二维小波和二维小波包技术在不同压缩模式下的降噪处理

背景:小波和小波包技术是进行时频信号分析的重要方法.医学图像数字化采集后断层多,数据信息量大,易受噪声影响.采用二维小波技术和小波包技术可以实现肝癌图像的完美压缩和降噪.目的:比较二维小波和二维小波包技术在不同压缩模式下压缩肝癌图像的优劣以及小波降噪的技巧.方法:选用同一幅动脉期肝癌图像,进行4层分解,利用二维小波和二维小波包技术分别进行Balance sparsity-norm、Remove nearO和Bal.sparsity-norm(sqrt)三种模式的压缩处理,再利用小波函数对含噪声信号的图像进行降噪处理.结果与结论:对于同一种压缩模式,二维小波包技术压缩肝癌图像优于二维小波技术,3种压缩模式中Bal.sparsity-norm(sqrt)模式和Remove nearO mode模式压缩比例更小,图像清晰度更好;小波降噪能很好地消除噪声信号.提示利用二维小波技术和小波包技术都可以实现肝癌图像的完美压缩和降噪.     

基于局部特征的医学图像融合方法

目的 介绍一种基于局部小波系数特征的多尺度医学图像融合方法.方法 首先对待融合的两幅医学图像做多尺度的小波分解,然后采用原始图像灰度的局部标准差作为小波系数选取的参考标准,最后再对选取的小波系数进行重构得到最终的融合图像.结果 成功将一幅MRI解剖图像和一幅SPECT功能图像融合在一起.结论 基于局部特征的医学图像融合方法是切实可行的,且简便灵活,图像融合效果较好.     

腹部CT及MRI图像融合配准在临床中的应用

目的：探索在PC平台上实现腹部CT及MRI图像非刚性融合配准，讨论其为临床提供新的诊断信息的应用价值。方法：根据最大互信息原理，对40例患者（男25例，女15例）腹部病变的CT及MRI图像进行融合，再根据图像信息主次性形成两类侧重点不同的融合配准图像。结果：在40例CT和MRI图像（其中有3例CT或MRI图像人为的造成部分缺失）融合中，能够在一幅图像上图像信息相互补充的有37例（人为造成部分缺失的3例顺利融合），比单纯地观察CT或MRI图像更能明确判断病变发展趋势的有30例，手术证实的15例，但有3例图像融合后无明显优越性。结论：微机实现腹部CT及MRI这两种不同来源的多模态图像非刚性融合配准，可为临床医生明确诊断、设计手术、放疗方案提供有利信息；病灶显示更直观，方便了临床医生观察。在融合算法上，最大互信息法几乎可以用在任何不同模式图像的配准，特别是当其中一个图像的数据部分缺损时也能得到很好的配准效果。     

基于像素融合的Curvelet医学超声图像降噪方法

目的：探求基于Curvelet变换的医学超声图像降噪的有效方法。方法：分别对超声图像进行Curvelet和隐Markov树小波降噪，再采用模糊分区方法对结果图像进行像素融合。结果：实现了基于像素融合的Curvelet医学超声图像降噪。结论：对超声图像的降噪实例表明该方法有效提高了图像的视觉效果，明显抑制了伪影。     

基于小波变换的医学超声图像去噪及增强方法

目的探求一种基于小波变换的医学超声图像去噪及增强方法。方法提出了一种基于小波分析理论的医学超声图像噪声的综合抑制方法,首先对医学超声图像进行对数变换,将乘性噪声变成加性噪声;然后进行多尺度小波变换,将图像分解成一系列不同尺度上的小波系数,对变换后不同尺度的高频子图像进行非线性小波软阈值处理,阈值处理后的高频子图像进行增强;最后,经小波逆变换和指数变换恢复去噪后图像。结果原图像中斑纹噪声被有效去除,图像边缘细节得以保留。结论该方法可有效保留细节信号,极大限度地去除斑纹噪声。     

基于小波变换的PET/CT图像融合算法研究进展

随着图像融合技术在影像学领域的不断发展,越来越多新的影像技术及成像设备出现。PET/CT将PET和CT两种模态的医学图像进行融合,优势互补,提高了医师诊断疾病的效率及准确率。小波变换在医学图像融合中有重要作用,基于小波变换的算法使融合后图像的细节更加清晰而易于诊断,已成为近年来医学图像融合领域中研究的重点。本文对基于小波变换的PET/CT图像融合算法的研究进展进行综述。     

基于融合图像的导航技术在子宫肌瘤射频消融术中的初步应用

目的探讨子宫肌瘤射频消融术中三维超声造影图像与实时二维超声图像融合实现导航的可行性。方法对30例子宫肌瘤患者进行超声造影,分析时间-强度曲线以确定开始采集三维超声造影图像的最佳时间点。再次行超声造影,在最佳时间点处采集三维超声造影图像并与实时二维超声图像融合,以子宫轮廓线重合度、融合图像肌瘤回声及肌瘤边缘清晰度对融合图像质量进行评价。应用基于融合图像的导航技术对子宫肌瘤进行穿刺消融,记录穿刺成功率。结果 30例患者开始采集三维超声造影图像的平均时间点为(75.5±9.5)s,即造影晚期。图像融合过程中子宫轮廓线完全重合者28例,完全重合率为93.3%。二维超声图像肌瘤均呈低或等回声,肌瘤边缘显示清晰者占70.0%;融合图像肌瘤均呈稍强回声,肌瘤边缘显示清晰者占93.3%,两种图像肌瘤边缘显示清晰度差异有统计学意义(P<0.05)。图像融合成功28例,成功率为93.3%。应用基于融合图像的导航技术对肌瘤进行消融,穿刺成功率为100%。结论图像融合过程中开始采集肌瘤三维超声造影图像的最佳时间点为(75.5±9.5)s,即造影晚期。融合图像质量可选用子宫轮廓线重合度、融合图像肌瘤回声及肌瘤边缘清晰度作为评价指标。子宫肌瘤射频消融术中将肌瘤三维超声造影图像与实时二维超声图像融合实现导航是可行的。     

基于非冗余平移不变小波变换的磁共振图像降噪

背景:MRI成像机制决定了其时间/空间分辨率和信噪比之间存在矛盾,因此图像降噪变得十分必要.目前基于离散小波变换的降噪方法广泛应用,然而存在平移敏感性的缺陷.目前已出现了克服平移敏感性的离散小波变换,但其冗余性导致计算复杂度的快速增加.目的:针对图像降噪设计小波滤波器,减小降采样过程的影响,保持离散小波变换的非冗余性,并针对MRI图像Rician噪声的降噪进行分析.方法:由于平移敏感性主要是由于离散小波变换分解时降采样产生的混叠项带来的,在保证非冗余的前提下,提出了通过减小混叠项的影响来减小平移敏感性.在此基础上,设计了一个双正交小波.最后,将其以常见的阈值降噪方法应用到磁共振图像Rician噪声的降噪中.结果与结论:文章提出了设计小波滤波器的新方法,即满足严格重构条件外满足一些附加要求,最后将设计过程简化为一个有约束条件的最优化过程.将设计的双正交小波应用于MR图像,仿真结果表明降噪效果较通常小波有所改善,间接表明了设计思路和方法的有效性.     

基于小波的磁共振图像聚类分割

目的 利用直方图自适应确定人体不同部位MRI的聚类类别的数目和相应的初始聚类中心,实现模糊-c均值聚类算法(FCM)分割的自适应。方法 首先采用小波变换拟合直方图的平滑包络线,降低噪声对寻找包络线极值的影响;其次根据微积分的知识求出包络线极大值的个数,按照文中给出的法则对包络线的极大值进行筛选,确定直方图中峰值的个数;最后以直方图中峰值的个数为聚类类别数,以相应的峰值为初始聚类中心,对MRI进行FCM分割。结果 采用该方法对多幅腹部和脑部MR图像进行分割,均能有效地自适应确定聚类的个数。结论 本文方法能够有效、准确地确定不同MR图像的聚类类别的个数,实现FCM的自适应。     

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms

Purpose

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms.

Methods

A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details.

Results

The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations.

Conclusion

In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

     

基于多尺度Retinex的医学图像增强方法研究

目的增强医学图像中的暗区信息和图像对比度,压缩医学图像的动态范围。方法提出并改进了基于多尺度Retinex方法的医学图像增强处理方法,在3种标准偏差下,求得高斯环境函数;然后使用3种不同的误差函数对医学图像进行卷积操作,将3种标准偏差尺度下得到的结果进行加权平均;最后将输出灰度值进行修正,得到可用于显示的结果。结果改进的MSR算法既可实现低对比度的医学图像增强,又能实现图像的动态范围压缩,能够显著提高暗区医学图像的信息。结论改进MSR算法能够显著提高暗区图像的信息量,其增强后的信息熵是原图像的2倍多,该方法克服了常规医学增强方法的不足,能够满足医生临床诊断的要求。     

Fast OCT image enhancement method based on the sigmoid-energy conservation equation

Optical coherence tomography (OCT) is an important medical diagnosis technology, but OCT images are inevitably interfered by speckle noise and other factors, which greatly reduce the quality of the OCT image. In order to improve the quality of the OCT image quickly, a fast OCT image enhancement method is proposed based on the fusion equation. The proposed method consists of three parts: edge detection, noise suppression, and image fusion. In this paper, the improved wave algorithm is used to detect the image edge and its fine features, and the averaging uncorrelated images method is used to suppress speckle noise and improve image contrast. In order to sharpen image edges while suppressing the speckle noise, a sigmoid-energy conservation equation (SE equation) is designed to fuse the edge detection image and the noise suppression image. The proposed method was tested on two publicly available datasets. Results show that the proposed method can effectively improve image contrast and sharpen image edges while suppressing the speckle noise. Compared with other state-of-the-art methods, the proposed method has better image enhancement effect and speed. Under the same or better enhancement effect, the processing speed of the proposed method is 2 ∼ 34 times faster than other methods.     

Progressive multi-atlas label fusion by dictionary evolution

Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain). However, due to the generally large gap between the patch appearance in the image domain and the patch structure in the label domain, the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer static dictionary to the multi-layer dynamic dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer static dictionary.     

采用图像匹配和三维跟踪器的人体经络可视化方法

背景：经络信息可视化技术采用图形图像与视觉的表现形式,将现实中不可见的人体经络显示于计算机屏幕上,使抽象的经络信息展现在人们面前,用以辅助经络定位和临床诊治。目的：为更方便有效地提供人体经络可视化方法,基于磁场跟踪器和摄像机标定技术,开发一种基于电阻抗的人体经络检测和可视化系统。方法：将多通道经络检测中各个通道的电阻抗与由磁场跟踪器所确定的相应通道电极探针触点的三维坐标进行配对,利用经络低电阻抗特性和代价函数从候选通道中选择准确的经络点并确定三维经络线;然后用＂张正友＂法和图形变换法进行优化,将经络线的三维坐标映射到人体皮肤表面的二维图像上;随着电极探头移动,整条经络线就可显示在皮肤表面的二维图像上。结果与结论：研究和实验结果表明,系统的阻抗检测误差小于0.2%,并可实时地在现场人体体表图像上准确有效地显示人体经络。该方法实现了人体经络的可视化,可用于医生临床诊断治疗或中医教学。     

Compressed sensing MRI exploiting complementary dual decomposition

Compressed sensing (CS) MRI exploits the sparsity of an image in a transform domain to reconstruct the image from incoherently under-sampled k-space data. However, it has been shown that CS suffers particularly from loss of low-contrast image features with increasing reduction factors. To retain image details in such degraded experimental conditions, in this work we introduce a novel CS reconstruction method exploiting feature-based complementary dual decomposition with joint estimation of local scale mixture (LSM) model and images. Images are decomposed into dual block sparse components: total variation for piecewise smooth parts and wavelets for residuals. The LSM model parameters of residuals in the wavelet domain are estimated and then employed as a regional constraint in spatially adaptive reconstruction of high frequency subbands to restore image details missing in piecewise smooth parts. Alternating minimization of the dual image components subject to data consistency is performed to extract image details from residuals and add them back to their complementary counterparts while the LSM model parameters and images are jointly estimated in a sequential fashion. Simulations and experiments demonstrate the superior performance of the proposed method in preserving low-contrast image features even at high reduction factors.     

Dual tree complex wavelet transform based denoising of optical microscopy images

Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.OCIS codes: (100.0100) Image processing, (100.7410) Wavelets, (100.3020) Image reconstruction-restoration