特征与分数阶微分驱动的光流计算及其在大形变脑图像配准中的研究

大形变脑图像配准因具有高复杂性、低精确度、低可靠性而成为脑图像配准技术中的难点。本项目针对脑图像的结构复杂、弱纹理丰富等特点，一方面研究特征点匹配与图像细节保持的映射模型，将其作为约束条件引入光流配准模型，实现特征约束的脑图像配准；另一方面研究脑图像分数阶微分模型，将其嵌入光流配准模型中进行梯度学习，提高弱纹理的脑图像配准精度；构建特征和分数阶微分共同驱动的光流配准模型，实现大形变脑图像的高精度配准。研究内容包括：大形变脑图像的有效特征提取与描述；基于改进的K-d树结合随机抽样一致性的高准确匹配算法；特征驱动下局部细节保持的高精度光流配准算法与分析；分数阶微积分的图像计算；基于分数阶微分的特征描述子算法；基于分数阶微分的光流模型计算。本研究不仅能在光流配准理论上有较大突破，而且对典型大形变脑图像配准提供理论基础和算法设计，具有学术意义和应用价值。

Brain image registration with large deformation has been a technical difficulty in brain image registration due to its high complexity, low accuracy and low reliability. For brain images having the characteristics of complex structure and rich texture, this project will study on: (1) the mapping relation of feature point matching and image details preserving, which is taken as a constraint combined into optical flow model to realize the brain image registration with feature constraints; (2) fractional order differential computation applied to brain image, which is embedded into optical flow to enhance image gradient and improve registration precision of brain image with weak texture. The project will build feature and fractional order differential driven optical flow to realize high precision registration of brain images with large deformation. The research contents include: Effective feature extraction and description of large deformable brain image; High accurate matching algorithm based on improved K-d tree combined with random sampling uniformity; High precision registration model of feature driven optical flow for local details preserving; Fractional order calculus calculation on image processing; Feature descriptor algorithm based on fractional order differential; Optical flow model design based on fractional order differential calculation. This study not only has a great breakthrough in optical flow theory but also provides theoretical foundation and algorithm design for brain image registration with typical large deformation, which has academic significance and practical application value.