OBJECT DEFINITION IN TOMOGRAPHIC RADIOLOGY

断层放射学中的对象定义

• 批准号: 6621458
• 负责人: JAYARAM K UDUPA
• 金额: $ 33.14万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-12-01 至 2005-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6621458
• 关键词: bioimaging /biomedical imaging; computational neuroscience; computed axial tomography; computer assisted diagnosis; diagnosis design /evaluation; human data; image processing; magnetic resonance imaging; multiple sclerosis; nervous system disorder diagnosis

DESCRIPTION (Provided by Applicant): The broad long-term goal of this project is to develop theories, algorithms and their practical computer implementations to identify and quantify object information captured in multidimensional medical images. In many radiological applications, the lack of cost-effective methods for this purpose with proven, acceptable precision and accuracy remains one of the major impediments to further advances. With this in mind, this proposal addresses three goals: (i) to standardize the MR image intensity scale so that object definition and tissue characterization is facilitated; (ii) to segment tissue regions in the brain into anatomic regions and into diseased tissue regions; (iii) to devise new MR image-based quantitative measures that would give more disease-specific information in Multiple Sclerosis (MS). The standardization method is based entirely on image processing (histogram deformation), rather than on phantoms, and hence it will be applicable to already acquired images also. The segmentation of tissue regions is done via an extension of the fuzzy connectedness method to multiple objects wherein it considers the relative connectedness among objects to delineate their fuzzy interfaces. The new measures to quantify the MS disease severity are based on the standardized image intensity distributions within tissue regions. MS is an acquired disease of the central nervous system whose cost to the US is estimated at $2.5 billion annually. A precise, accurate, cost-effective image-based method of determining the disease severity is currently lacking. The proposed methods may not only help in understanding MS and its progression but also in determining the effectiveness of various therapies.

描述（由申请人提供）：该项目的长期目标 是开发理论、算法及其实用的计算机实现 识别和量化多维捕获的物体信息 医学图像。在许多放射学应用中，缺乏具有成本效益的 用于此目的的方法仍然具有经过验证的、可接受的精度和准确度 进一步发展的主要障碍之一。考虑到这一点，这 提案涉及三个目标：(i) 标准化 MR 图像强度等级 以便于对象定义和组织表征； (二) 至 将大脑中的组织区域分割成解剖区域和患病区域 组织区域； (iii) 设计新的基于 MR 图像的定量测量方法 将提供更多有关多发性硬化症 (MS) 的疾病特异性信息。 标准化方法完全基于图像处理（直方图 变形），而不是幻像，因此它将适用于 也已经获取了图像。组织区域的分割是通过 将模糊连通性方法扩展到多个对象，其中 考虑对象之间的相对关联性来描绘它们的模糊性 接口。量化多发性硬化症严重程度的新措施基于 组织区域内的标准化图像强度分布。 多发性硬化症是一种中枢神经系统获得性疾病，其给美国造成的损失是 估计每年25亿美元。精确、准确、经济高效 目前缺乏基于图像的确定疾病严重程度的方法。 所提出的方法不仅有助于理解 MS 及其进展 还可以确定各种疗法的有效性。