基于联合显著图和局部自适应核回归的非刚性医学图像配准及临床应用研究

项目名称： 基于联合显著图和局部自适应核回归的非刚性医学图像配准及临床应用研究

项目编号： No.61271320

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 秦斌杰

作者单位： 上海交通大学

项目金额： 76万元

中文摘要： 多模式多时段医学影像技术的发展，和临床对肿瘤病灶、心肌灌注成像等复杂图像定量分析的紧迫需求，促使本项目要解决好在局部大形变和组织对应性缺失的复杂异常条件下、现有非刚性医学图像配准算法大都失效的问题：1)基于待配准图像局部显著结构及其相似性表示构建联合显著图，准确区分已配准好结构区域、配准残差大的局部大形变和对应性缺失结构区域以及背景区域。进一步提出新的结构相似性配准测度和配准残差减少反馈机制，来迭代提升非刚性医学图像配准精度。2)利用多尺度子块匹配对局部各向异性尺度、方位和局部相似性测度的自适应学习，以及各像素对基于联合显著图的"确定度"分配，局部自适应核回归无监督地精确配准医学图像的局部显著结构(或连贯分布图像特征)。3)从自适应核回归基函数和非对称核函数、多点逼近多元核回归、以及低维流形核回归等角度，综合研究非刚性医学图像配准的非参数化处理新理论和新方法，顺应非参数化图像处理研究趋势。

中文关键词： 非刚性图像配准；联合显著结构自适应核回归；边缘感知联合显著图；异常结构；图像分解

英文摘要： To meet the urgent demands for tracking and quantifying the lession tissue changes in modern clinical application, nonrigid registration of multimodal and multitemporal images often meets great challenges due to the great geometric difference between the images introduced by the local large deformation and missing correspondence (or outliers) of local tissues in the same scene. In order to tackle these problems, this project proposed following novel methods: 1) By adaptively learning the local structures and their similarity measures from the two images to be registered, we propose a novel joint saliency map (JSM) as a new structural similarity measure in nonrigid registration to distinct the non-correspondence structures from correspondence structures and backgournd, with the JSM representing registration bias in these non-correspondence structures to iteratively reduce registration errors and boost the registration performance in registration feedback iteration. 2) We further introduce a novel nonrigid registration framework, based on joint-saliency structure adaptive kernel regression, to aim at handling local complex large deformation and outliers. The sparse displacement via multi-resolution block matching is locally and adaptively smoothed according to the structural adaptive kernel function which is adap

英文关键词： nonrigid image registration；joint saliency structure；adaptive kernel regression；edge-aware joint saliency map；outlier structures