基于PCA与二代Curvelet变换的多模态医学图像融合方法研究

项目名称： 基于PCA与二代Curvelet变换的多模态医学图像融合方法研究

项目编号： No.61262034

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 杨勇

作者单位： 江西财经大学

项目金额： 50万元

中文摘要： 多模态医学图像融合是当前医学图像处理领域中的研究热点和难点。第二代Curvelet变换有效克服了传统二维小波变换在方向性、稀疏性等方面的不足，反映了近年来图像多尺度几何分析的最新进展。本项目在前期研究基础上，针对Curvelet变换及其在结构和功能医学图像融合中的应用进行如下研究：针对Curvelet变换的移变性缺陷并结合融合医学图像的特性提出一种基于PCA+Curvelet变换且具有"移不变性"的多模态医学图像融合新方法；为保证融合过程的鲁棒性和自适应性，针对分解系数的低、高频不同物理特性，提出一种基于区域相似度的低频系数融合方法和一种基于邻域熵的PCNN高频系数融合方法；最后，基于人类视觉系统特性，研究新的融合质量评价方案。本项目的研究将为结构和功能医学图像融合提供新的研究思路和方法，其结果将进一步提高医学图像融合的质量和性能，对于多模态医学图像融合在临床诊断和医学研究上具有重要价值。

中文关键词： 图像融合；多模态医学图像；Curvelet 变换；非下采样Contourlet变换；模糊逻辑

英文摘要： The multimodal medical image fusion is the current research hot spot and difficulty in the field of medical image processing. The second generation Curvelet transform that effectively overcomes the shortcoming of the traditional two-dimensional wavelet transform in the direction and sparsity reflects the latest developments of multiscale geometric analysis in recent years. Based on our preliminary studies, this project will focus on the Curvelet transform and its applications in the fusion of structural and functional medical images for the following research. Firstly, in view of the shift-variant defect of the Curvelet transform and the characteristics of the medical images to be fused, a novel shift-invariant multimodal medical image fusion method is presented which is based on the PCA (Principal component analysis) and Curvelet transform. Secondly, in order to guarantee the robustness and adaptability of the fusion process, different fusion strategies are developed for the decomposed coefficients of low and high frequency bands according to their different physical properties. In the project, a regional similarity based method is proposed to fuse the low frequency coefficients, and a neighborhood entropy based PCNN (Pulse-coupled neural network) method is put forward to fuse the high frequency coefficients. F

英文关键词： Image fusion；multimodal medical image；Curvelet transform；non-subsampled Contourlet transform；fuzzy logic