A new framework for multiscale and multiorientation image analysis and its application to medical volume data analysis

该项目研究了医学体数据中血管等线状形状和关节软骨等片状形状的增强、分割和量化的基本框架和临床应用。基于多尺度高斯滤波和体积函数的海森矩阵，对不同宽度的线条和片状结构进行了增强处理。通过进一步组合梯度向量，将局部强度结构表示为连续函数。基于局部强度结构的二阶近似，局部确定中轴/表面单元。基于检测到的中轴/表面元素执行直径/厚度量化，并通过多尺度积分组合不同宽度的结构。该框架被应用于基于肺结节周围血管的空间分布来表征肺结节，以及使用真实的计算机断层扫描(CT)和磁共振(MR)体积数据来确定关节软骨厚度。

This project investigated a basic framework and clinical applications for the enhancement, segmentation, and quantification of line-like shapes such as blood vessels and sheet-like shapes such as articular cartilage in medical volume data. Based on multiscale Gaussian filters and the Hessian matrix of the volume function, line and sheet structures with various widths are enhanced. Local intensity structures are represented as continuous functions by further combining the gradient vectors. Medial axis/surface elements are locally determined based on the second-order approximations of local intensity structures. Diameter/thickness quantification is performed based on detected medial axis/surface elements, and various widths of structures are combined through multiscale integration. The utility of the framework was demonstrated by applications to the characterization of pulmonary nodules based on the spatial distribution of vessels surrounding pulmonary nodules, and articular cartilage thickness determination using real computed tomography (CT) and magnetic resonance (MR) volume data.