基于小波分解的多尺度医学图像融合技术

给出了一种基于小波分解的多尺度图像融合新方法。其基本思想是 ,先对源图像进行小波多尺度分解 ;其次 ,采用了基于区域特性量测选择的加权算子的融合规则进行小波系数融合 ;最后通过小波逆变换重构融合图像。实验结果表明 ,该融合方法十分有效 ,融合图像完好地显示了源图像各自的信息。     

基于在线字典学习和脉冲耦合神经网络的脑图像融合

医学图像融合是医学影像和放射医学等领域的研究热点之一,广受医学界和工程界重视。提出一种基于在线字典学习(ODL)和脉冲耦合神经网络(PCNN)的脑部CT和MR图像融合新算法。首先,利用滑动窗技术将源图像分块,使用ODL算法和最小角回归算法(LARS)得到各图像块对应列向量的稀疏编码;其次,将稀疏编码作为脉冲耦合神经网络的外部输入刺激信号进行迭代处理,根据点火次数确定融合系数;最后,根据融合系数和学习字典重构融合图像。基于哈佛医学院的10组脑部CT和MR数据,将所提出算法同基于KSVD的融合算法、基于ODL的融合算法、基于NSCT的融合算法比较。实验结果显示：综合考虑主观视觉效果和客观评价指标,该算法性能整体优于其他算法,客观参数指标BSSIM、MI、Piella、SF、STD、Q^AB/F的均值分别为0.751 2、3.769 6、0.697 1、29.526 7、90.090 6、0.570 7,可以提供丰富的信息来辅助医生分析病变体,提高临床医疗诊断的准确性和治疗规划的科学性。     

MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules

Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to evaluate the fusion result. In visual evaluation the opinion of two radiologists was used and in quantitative evaluation the proposed fusion method was compared with six existing methods and used criteria were entropy, mutual information, discrepancy and overall performance.     

肝包虫病和肝囊肿CT图像的分型研究

探讨计算机辅助诊断技术在肝包虫病和肝囊肿CT图像分型中的应用。方法 对单囊型肝包虫病和单发性肝囊肿CT图像感兴趣区域,分别使用传统的预处理方法和图像融合方法,提取原始ROI、预处理后的和融合后的ROI图像Haar小波、DB2小波、Tamura、Gabor滤波器和灰度-梯度共生矩阵特征,通过支持向量机和BP神经网络分类模型分类,比较三种方法的分类准确率,并对各分类模型进行参数评估。结果 从原始ROI图像直接提取的Haar小波、DB2小波、Tamura和GGCM特征的最佳分类准确率均达到了95%以上;融合后的ROI图像五种特征的分类准确率都较高,在90%以上。结论 本研究所使用的方法应用于肝包虫病和肝囊肿CT图像的分型中具有一定的分类优势,为影像学诊断提供依据。     

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain

Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.     

A Novel Medical Image Quality Index

A novel medical image quality index using grey relational coefficient calculation is proposed in this study. Three medical modalities, DR, CT and MRI, using 30 or 60 images with a total of 120 images used for experimentation. These images were first compressed at ten different compression ratios (10 ∼ 100) using a medical image compression algorithm named JJ2000. Following that, the quality of the reconstructed images was evaluated using the grey relational coefficient calculation. The results were shown consistent with popular objective quality metrics. The impact of different image aspects on four grey relational coefficient methods were further tested. The results showed that these grey relational coefficients have different slopes but very high consistency for various image areas. Nagai’s grey relational coefficient was chosen in this study because of higher calculation speed and sensitivity. A comparison was also made between this method and other windows-based objective metrics for various window sizes. Studies found that the grey relational coefficient results are less sensitive to window size changes. The performance of this index is better than some windows-based objective metrics and can be used as an image quality index.     

CT与磁共振图像融合技术对显示正常内耳结构的应用价值

目的 探讨内耳CT与磁共振成像(MRI)图像融合的可行性以及融合图像对正常内耳结构的显示能力.方法 对20例(40耳)双侧内耳及内听道形态正常的感音神经性耳聋患者行多层螺旋CT(MSCT)和MRI扫描,并应用西门子图像融合软件将CT与MRI图像进行配准、融合,然后半定量评价融合图像对正常内耳和内听道结构的显示能力.结果 CT与MRI融合可有9种融合模式,在不同融合模式的图像中CT不反相+MRI反相对骨迷路显示最清晰;CT反相+MRI不反相、CT反相+MRI伪彩对膜迷路显示最清晰、立体感强;CT不反相+MRI反相、CT与MRI均反相对面神经、听神经及蜗神经显示最清晰、最直观、立体感最强;CT不反相+MRI反相、CT反相+MRI不反相、CT伪彩+MRI反相对蜗孔处蜗神经显示最好.结论 CT与MRI图像可以进行精确配准和融合,不同模式融合图像对内耳不同结构的显示清晰度不同.     

基于双稀疏字典的医学图像融合算法及在脑血管疾病诊断中的应用

目的通过图像融合方法结合解剖和功能医学图像以提供更多有用的信息并辅助医生诊断。方法利用稀疏表示能很好地反映图像特征的优势。首先,选取医院脑梗死和脑出血的CT和MRI的临床图像,采用双稀疏字典算法得到稀疏字典,再通过结合空间域信息的最大选择法作为融合规则对其进行融合,并与基于主成分分析(principal component analysis,PCA)和离散小波变换(discrete wavelet transform,DWT)方法的图像融合结果在主观方面以及客观方面的QAB/F和Piella指标上进行比较。结果本文提出的方法所获得的融合图像主观评价优于另外两种方法。QAB/F和Piella的均值分别为0.9139和0.7213,客观评价指标也优于另外两种方法。结论基于双稀疏字典的图像融合算法得到的融合图像更清晰,对比度更高,并且特征保留效果更好,有助于医生的诊断。     

基于三次样条层间插值的多模态医学图像配准

我们从PET-CT多模态图像序列的特点出发,提出了一种全新的图像配准及融合方法,它采用三次样条插值法对PET-CT图像进行层间插值,然后再利用最大互信息法进行配准,最后应用改进的主成分分析(PCA)法融合PET-CT图像用以增强PET显像效果,从而得到满意的配准以及融合结果。用三次样条插值法进行层间插值并恢复层间缺失图像的信息,弥补了现有配准方法的不足,提高了配准精度,使融合后的图像更加接近实际的物理断层。该方法已经成功应用于三维适形放疗(3D-CRT)系统的开发中。     

基于层次式优化的多幅眼底图像融合方法

针对眼底图像配准后直接叠加产生的接缝及如何保证多幅图像融合后细节信息不丢失的问题,提出层次式优化的多频带眼底图像融合方法。该方法通过多阈值分割获取掩模图像并计算其欧式距离得到各层次图像蒙版;根据欧氏距离值及拉普拉斯能量和设计每层蒙版图像的改进加权系数。构建基于信息熵、空间频率和清晰度的图像融合联合客观评价方法进行分析。最后,利用配准误差及重叠率对图像序列进行分组融合后进入下一层,对于奇数幅图像序列中没有组合的图像直接放入下一层。再根据分组规则重新分组进行优化融合,实现层次式的多幅眼底图像优化融合。通过对75 组取自福建省附属第一医院眼科及眼底相机实验系统的图像序列,涉及4 898 组图像对（正常眼底图像2 952 对,病变眼底图像1 946 对）进行测试与验证,结果表明,提出方法在有效除去拼接缝的同时,融合图像在重叠区域的RMSE值约为（01±005）像素。提出的融合方法在客观评价和主观视觉效果之间取得了较好的平衡。     

Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation

In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure.     

基于自由变形法的多模态医学图像的配准与融合

本研究提出了一种自动识别颈部PET-CT图像特征点的算法，它应用自由变形（FFD）方法以CT图像的特征点为参考使PET图像产生变形，再结合最大互信息法对颈部PET与CT图像进行非刚体配准，最后用改进的小波图像融合法把两者进行融合得出视觉效果比较理想的融合图像。经实际计算得出的变形PET图像与对应CT图像的互信息量大于原始PET图像，并且最后用改进的小波图像融合法得出的融合图像的信息量比一般小波融合大，由此证明本研究所用方法是有效的。     

Visible Human图象数据集的自动匹配方法

VisibleHuman计划是由美国国家医学图书馆发起建立的真实三维入体的CT、MRI和解剖切面的数字化图象库。Visible Human数据集已经成为构造电子医学图象库和虚拟解剖环境的理想基础,本文首先对CT图象进行灰度校正,并用基于像素相似性的方法实现了CT图象和MRI图象间的自动匹配。具有自适应空间搜索的遗传算法被用于实现全局性的优化过程,本文给出了初步的实验结果。     

Pixel-Feature Hybrid Fusion for PET/CT Images

Color blending is a popular display method for functional and anatomic image fusion. The underlay image is typically displayed in grayscale, and the overlay image is displayed in pseudo colors. This pixel-level fusion provides too much information for reviewers to analyze quickly and effectively and clutters the display. To improve the fusion image reviewing speed and reduce the information clutter, a pixel-feature hybrid fusion method is proposed and tested for PET/CT images. Segments of the colormap are selectively masked to have a few discrete colors, and pixels displayed in the masked colors are made transparent. The colormap thus creates a false contouring effect on overlay images and allows the underlay to show through to give contours an anatomic context. The PET standardized uptake value (SUV) is used to control where colormap segments are masked. Examples show that SUV features can be extracted and blended with CT image instantaneously for viewing and diagnosis, and the non-feature part of the PET image is transparent. The proposed pixel-feature hybrid fusion highlights PET SUV features on CT images and reduces display clutters. It is easy to implement and can be used as complementarily to existing pixel-level fusion methods.     

Compressed sensing MRI with singular value decomposition-based sparsity basis

Compressed sensing MRI (CS-MRI) aims to significantly reduce the measurements required for image reconstruction in order to accelerate the overall imaging speed. The sparsity of the MR images in transformation bases is one of the fundamental criteria for CS-MRI performance. Sparser representations can require fewer samples necessary for a successful reconstruction or achieve better reconstruction quality with a given number of samples. Generally, there are two kinds of 'sparsifying' transforms: predefined transforms and data-adaptive transforms. The predefined transforms, such as the discrete cosine transform, discrete wavelet transform and identity transform have usually been used to provide sufficiently sparse representations for limited types of MR images, in view of their isolation to the object images. In this paper, we present singular value decomposition (SVD) as the data-adaptive 'sparsity' basis, which can sparsify a broader range of MR images and perform effective image reconstruction. The performance of this method was evaluated for MR images with varying content (for example, brain images, angiograms, etc), in terms of image quality, reconstruction time, sparsity and data fidelity. Comparison with other commonly used sparsifying transforms shows that the proposed method can significantly accelerate the reconstruction process and still achieve better image quality, providing a simple and effective alternative solution in the CS-MRI framework.     

基于多小波变换的医学图像融合算法研究

目的:研究一种基于多小波变换的医学影像融合的算法。方法:对已配准的PET图像和CT图像进行预滤波后进行多小波分解,对分解后的图像低频分量采用平均梯度法及高频分量采用自适应加权法的融合规则进行图像融合,经过多小波重构及后滤波得到融合图像。结果:融合图像通过结合源图像的信息,增加了更多的细节和纹理信息,从而得到了良好的融合效果。结论:实验证明,基于该算法,可以得到图像的最佳融合结果。     

SPECT-CT同机图像融合的仪器质量控制方法探讨

目的 探讨SPECT-CT同机图像融合硬件配准误差的质量评价方法.方法 制作带外套点源、点线模型及用ECT模型,加注放射源后,在床有、无负重情况下,分别对模型进行SPECT-CT采集,然后进行SPECT-CT同机图像融合处理,分析两种图像融合偏差程度.结果 SPECT与CT旋转中心无偏移.在床无负重情况时,点源及点线源与外套无X、Y轴偏移.在床有负重情况下,融合图像Y轴偏移最大值为8.0 mm,X轴偏差无像素偏移.ECT模型可检测核医学"冷"区图像及整体图像融合情况,但不能进行定量测量偏移情况.结论 单点源模型可用于日常简单检测SPECT-CT图像融合硬件配准情况;点线源模型可用于准确定量分析图像融合硬件配匹程度;ECT模型可用图像融合质量综合分析.     

SPECT-CT同机图像融合的仪器质量控制方法探讨

目的探讨SPECT-CT同机图像融合硬件配准误差的质量评价方法。方法制作带外套点源、点线模型及用ECT模型,加注放射源后,在床有、无负重情况下,分别对模型进行SPECT-CT采集,然后进行SPECT-CT同机图像融合处理,分析两种图像融合偏差程度。结果SPECT与CT旋转中心无偏移。在床无负重情况时,点源及点线源与外套无X、Y轴偏移。在床有负重情况下,融合图像Y轴偏移最大值为8.0mm,X轴偏差无像素偏移。ECT模型可检测核医学"冷"区图像及整体图像融合情况,但不能进行定量测量偏移情况。结论单点源模型可用于日常简单检测SPECT-CT图像融合硬件配准情况;点线源模型可用于准确定量分析图像融合硬件配匹程度;ECT模型可用图像融合质量综合分析。     

利用相关比相似性测度多分辨率配准MR和CT医学图像的方法

本文提出了有效的、能被临床应用所接受的磁共振（MR）和CT医学图像配准方法。在基于体素灰度的医学图像配准领域。本文采用了全新的相关比相似性测度作为配准的测度准则。具体设计时，采用了加速的多分辨率配准方案，对方案中涉及的几何变换选取、重采样、多分辨率体数据表达及最优化方法进行了设计分析。最后，利用本文提出的多分辨率配准方法，对MR和CT临床医学图像进行配准，给出了令人满意的效果。     

CT与MRI图像融合的评价方法研究

医学图像融合的效果评价方法有很多,如信息熵、互信息、均方值、峰值信噪比和平均梯度等.但是对于不同的用途,难以给出一个通用的标准.根据CT与MR图像的不同特点,我们对CT与MR图像融合的评价方法进行研究.在分析实验数据的基础上,针对特殊的应用目的,给出了CT与MR图像融合效果评价方法的一个较全面的和有参考价值的选取准则.