Statistical Data Mining for Medical Image Data Analysis

医学图像数据分析的统计数据挖掘

• 批准号: 1851762
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1851762
• 关键词: Statistical; Data; Mining; Medical; Image

The aim of this project is to develop new statistical data mining models and methods for medical image data analysis. For example, Diffusion Tensor Imaging (DTI) is an advanced magnetic resonance imaging (MRI) method that benefits clinicians in monitoring human brain disease progression, planning neurosurgery and studying brain development. DTI data analysis must account for the diffusion tensor, which has a complicated mathematical structure, the three-dimensional spatial characteristics of the image, and the existence of complicated fibre structures in the brain. The combination of these aspects makes diffusion tensor image analysis one of the most challenging topics in the area of medical image analysis. Another example is mammography data analysis. To identify mammographic feature (e.g. mass, calcifications and architecture distortion) and to analyse the geometry properties of these features among mammography images are essential for breast cancer screening. To achieve the aim of this project, new techniques will be developed building on the rapidly developing theory of Statistical Date Mining, as well as on Statistical Shape Analysis and Computer Vision.

本项目旨在为医学影像数据分析开发新的统计数据挖掘模型和方法。例如，弥散张量成像（DTI）是一种先进的磁共振成像（MRI）方法，有利于临床医生监测人类脑部疾病的进展，计划神经外科手术和研究大脑发育。DTI数据分析必须考虑到弥散张量，它具有复杂的数学结构，图像的三维空间特征，以及大脑中复杂纤维结构的存在。这些方面的结合使得扩散张量图像分析成为医学图像分析领域中最具挑战性的课题之一。另一个例子是乳房x光检查数据分析。确定乳房x线摄影特征（如肿块、钙化和结构扭曲）并分析这些特征在乳房x线摄影图像中的几何特性对乳腺癌筛查至关重要。为了实现该项目的目标，将在快速发展的统计数据挖掘理论以及统计形状分析和计算机视觉的基础上开发新技术。