恶性肺结节的分类不确定性信息可视化传递函数研究

项目名称： 恶性肺结节的分类不确定性信息可视化传递函数研究

项目编号： No.61305038

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 李彬

作者单位： 华南理工大学

项目金额： 25万元

中文摘要： "分类不确定性可视化技术"传递和揭示恶性肺结节的三维视觉和量化信息，对肺癌诊疗至关重要。为了解决多模态成像中恶性肺结节的"分类不确定性的可视化"这个科学问题，项目拟通过图像融合、信息可视化、肺结节分割及良恶性识别技术构建"基于融合信息特征驱动"的"分类不确定性体可视化"传递函数"。主要研究内容包括：1）构建"模糊集成活动轮廓模型"和"血管粘连性结节的复合参数混合模型"，以及基于该模型的肺结节一体化分割算法；2）构建描述三维离散平移不变剪切波变换（SIST）中"融合系数的全局依赖关系"的模型，以及基于三维离散SIST变换和"全局－局部融合规则"的多模态成像融合方法；3）基于重要性驱动(importance-driven)编码、不确定性编码和风险代价函数，构建"基于融合信息特征驱动"的"分类不确定性可视化"多维传递函数。本项目为实现恶性肺结节的分类不确定性可视化、提高肺癌诊疗水平提供技术支撑。

中文关键词： 恶性肺结节；信息可视化；分类不确定性；传递函数；计算机辅助诊断

英文摘要： Classification uncertainty visualization techniques for malignant pulmonary nodules play an important role in the diagnosis of lung cancer, which can explore and communicate 3D quantitative and visual information about nodules,including the positioning and the relation between the nodule and its adjacent tissues.The goal of the project is to solve the research problem of "classification uncertainty visualization" for malignant pulmonary nodules in multi-modal medical imaging, and to provide technical support for clinical applications of lung cancer. By using techniques of image fusion, information visualization, segmentation and recognition of malignant pulmonary nodules, we aim at constructing the fusion-information-feature-driven transfer function of classification uncertainty visualization. The main research contents include: 1)Constructing the Hybrid Parametric Mixture Model (HPMM) of juxta-vascular nodules and the variational cost function of Fuzzy Integrated Active Contour Model(FIACM), and investigating the segmentation method of different types of pulmonary nodules in a unified framework, which is based on the FIACM model and the HPMM model for blood vessels and attached nodules. 2)Constructing the global dependency relationship model of three dimensional(3D) discrete shift-invariant shearlet coefficient

英文关键词： Malignant pulmonary nodule；Information visualization；Classification uncertainty；Transfer function；Computer-aided diagnosis