人脑MRI和PET图像的融合方法

目的：研究MRI和PET医学图像融合的方法并且应用在人脑神经解剖中。方法：以Chamfer matching为基本配准方法和以模糊数学为理论基础的融合方法。结果：使得融合后图像有很强的抗配准偏差能力，并且能极大程度地保留原来解剖性信息图像和功能性图像的信息。能根据融合算子的不同组合，得到倾向于不同检查仪器结果的图像。结论：通过本文方法得出的融合后图像，能在人脑解剖结构上显示出该区域的功能状态，对于了解该结构的存在、变异、病变提供了很大的便利。对人脑神经解剖学研究和临床应用都有很大的理论和实践意义。     

An Improved Medical Image Fusion Algorithm for Anatomical and Functional Medical Images

In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data, but, not an appropriate fusion algorithm for anatomical and functional medical images. In this paper, the traditional method of wavelet fusion is improved and a new fusion algorithm of anatomical and functional medical images,in which high-frequency and low-frequency coefficients are studied respectively. When choosing high-frequency coefficients, the global gradient of each subimage is calculated to realize adaptive fusion,so that the fused image can reserve the functional information;while choosing the low coefficients is based on the analysis of the neighborbood region energy, so that the fused image can reserve the anatomical image＇ s edge and texture feature. Experimental results and the quality evaluation parameters show that the improved fusion algorithm can enhance the edge and texture feature and retain the function information and anatomical information effectively.     

基于小波变换的多模态医学图像的融合及性能评价

为了研究小波变换分解的尺度和融合策略对图像融合效果的影响。我们选择已配准后的多聚焦医学图像以及MRI/CT灰度图像,在提取图像的低频和高频小波系数时,分别进行单尺度和多尺度分解,融合时采取了基于独立像素点和基于邻域窗口的多种融合策略,深入对比分析各种融合规则对医学图像融合性能的影响。实验结果和性能评价表明:使用局部滤波的操作可以明显改善图像融合的效果,使图像的细节信息更加丰富,而多尺度融合能明显提高融合图像的亮度。     

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。     

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.     

融合图像放疗靶区定位精度的检验和初步临床结果

目的：探讨以图像融合技术为基础的肿瘤三维适形放疗靶区定位精度的检验及依据融合图像放疗靶区的确定与单纯CT影像放疗靶区确定的初步临床结果。方法：利用定制的模体分别行CT、MRI和PET成像，进行CT与MRI，CT与PET融合。检验融合后定制标记点的定位精度。对3例特殊病例分别以单纯CT图像为基础和融合图像为基础，进行三维适形放疗靶区认定，对不同医生之间和同一医生在不同时间，放疗靶区定义情况进行对照分析。结果：MRI／CT融合图像总定位精度小于2mm，PET／CT图像融合图像融合精度情况（包括同机融合和异机融合），采用不同的融合算法。定位精度有显著差异（P〈0．01，t=5．385）。单纯利用CT图像进行靶区的定义，不同医生之间，在不同的时间存在差异（P〈0．05），而采用融合技术可减少他们的争议和差异。结论：利用多模式图像融合可以提高靶区定义的准确性．有利于三维适形精确放射治疗。     

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。     

Histology image search using multimodal fusion

This work proposes a histology image indexing strategy based on multimodal representations obtained from the combination of visual features and associated semantic annotations. Both data modalities are complementary information sources for an image retrieval system, since visual features lack explicit semantic information and semantic terms do not usually describe the visual appearance of images. The paper proposes a novel strategy to build a fused image representation using matrix factorization algorithms and data reconstruction principles to generate a set of multimodal features. The methodology can seamlessly recover the multimodal representation of images without semantic annotations, allowing us to index new images using visual features only, and also accepting single example images as queries. Experimental evaluations on three different histology image data sets show that our strategy is a simple, yet effective approach to building multimodal representations for histology image search, and outperforms the response of the popular late fusion approach to combine information.     

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。     

CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Fusion of CT and MR images allows simultaneous visualization of details of bony anatomy provided by CT image and details of soft tissue anatomy provided by MR image. This helps the radiologist for the precise diagnosis of disease and for more effective interventional treatment procedures. This paper aims at designing an effective CT and MR image fusion method. In the proposed method, first source images are decomposed by using nonsubsampled contourlet transform (NSCT) which is a shift-invariant, multiresolution and multidirection image decomposition transform. Maximum entropy of square of the coefficients with in a local window is used for low-frequency sub-band coefficient selection. Maximum weighted sum-modified Laplacian is used for high-frequency sub-bands coefficient selection. Finally fused image is obtained through inverse NSCT. CT and MR images of different cases have been used to test the proposed method and results are compared with those of the other conventional image fusion methods. Both visual analysis and quantitative evaluation of experimental results shows the superiority of proposed method as compared to other methods.