面向医学图像融合的多尺度特征频域分解滤波  

Multi-scale Feature Frequency Domain Decomposition Filtering for Medical Image Fusion

作　　者：刘慧[1,2] 朱积成[1,2] 王欣雨 盛玉瑞 张彩明 聂礼强[2,5] LIU Hui;ZHU Ji-Cheng;WANG Xin-Yu;SHENG Yu-Rui;ZHANG Cai-Ming;NIE Li-Qiang(School of Computer Science and Technology,Shandong University of Finance and Economics,Jinan 250014,China;Shandong Key Laboratory of Digital Media Technology(Shandong University of Finance and Economics),Jinan 250014,China;The First Affiliated Hospital of Shandong First Medical University,Jinan 250014,China;School of Software,Shandong University,Jinan 250101,China;School of Computer Science,Harbin Institute of Technology,Shenzhen,Shenzhen 518055,China)

机构地区：[1]山东财经大学计算机科学与技术学院,山东济南250014 [2]山东省数字媒体技术重点实验室(山东财经大学),山东济南250014 [3]山东第一医科大学第一附属医院,山东济南250014 [4]山东大学软件学院,山东济南250101 [5]哈尔滨工业大学(深圳)计算机学院,广东深圳518055

出　　处：《软件学报》2024年第12期5687-5709,共23页Journal of Software

基　　金：国家自然科学基金(62072274,U22A2033);中央引导地方科技发展项目(YDZX2022009);山东省泰山学者特聘专家计划(tstp20221137)。

摘　　要：多模态医学图像融合技术可以实现不同模态数据反映的组织结构与病变信息的融合,为后续医疗诊断、手术导航等临床应用提供更为全面和准确的医学图像分析.针对现有融合方法中存在的部分光谱退化、黏连病变侵袭区域边缘和细节缺失和色彩还原不足等问题,提出一种在多尺度特征频域分解滤波域内实现图像多特征增强和色彩保留的多模态医学图像融合方法.该方法将源图像分解为平滑、纹理、轮廓和边缘这4个特征层,分别利用特定融合规则并通过图像重构产生融合结果.特别地,鉴于平滑层所含潜在特征信息,提出视觉显著性分解策略,多尺度多维度地挖掘平滑层图像能量、部分纤维纹理等特征,提升源图像信息利用率;在纹理层中,提出纹理增强算子,通过空间结构和信息度量提取细节及其层次信息,解决现有融合方法中对黏连病变区域侵袭状态难以区分等问题.此外,针对缺乏公开腹部数据集的问题,配准403组腹部图像可供公开访问和下载.在Atlas公开数据集和腹部数据集上与6种基准方法对比及消融实验结果表明,所提方法相较于最先进的方法在融合图像与源图像相似度提升22.92%,边缘保持度提升35.79%,空间频率提升28.79%,对比度提升32.92%,并在视觉和计算效率方面有较好的效果,明显优于其他方法.Multi-modal medical image fusion provides a more comprehensive and accurate medical image description for medical diagnosis,surgical navigation,and other clinical applications by effectively combining human tissue structure and lesion information reflected by different modal datasets.This study aims to address partial spectral degradation,lack of edges and details and insufficient color reproduction of adhesion lesion-invaded regions in current fusion methods.It proposes a novel multi-modal medical image fusion method to achieve multi-feature enhancement and color preservation in the multi-scale feature frequency domain decomposition filter domain.This method decomposes the source image into four parts:smoothing,texture,contour,and edge feature layers,which employ specific fusion rules and generate fusion results by image reconstruction.In particular,given the potential feature information contained in the smoothing layer,the study proposes a visual saliency decomposition strategy to explore the energy and partial fiber texture features with multi-scale and multi-dimensionality,enhancing the utilization of source image information.In the texture layer,the study introduces a texture enhancement operator to extract details and hierarchical information through spatial structure and information measurement,addressing the issue of distinguishing the invasion status of adherent lesion areas in current fusion methods.In addition,due to the lack of a public abdominal dataset,403 sets of abdominal images are registered in this study for public access and download.Experiments conducted on public dataset Atlas and abdominal datasets are compared with six baseline methods.Compared to the most advanced methods,the results show that the similarity between the fused image and the source image is improved by 22.92%,the edge retention,spatial frequency,and contrast ratio of fused images are improved by 35.79%,28.79%,and 32.92%,respectively.In addition,the visual and computing efficiency of the proposed method are better than thos

关 键 词：医学图像融合 多尺度特征频域分解滤波 视觉显著性分解策略 纹理增强算子 多模态腹部数据集 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]