OBJECT DEFINITION IN TOMOGRAPHIC RADIOLOGY

断层放射学中的对象定义

• 批准号: 6092174
• 负责人: JAYARAM K UDUPA
• 金额: $ 5万
• 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 2000-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6092174
• 关键词: bioimaging /biomedical imaging; computational neuroscience; computer assisted diagnosis; computer system design /evaluation; human data; image enhancement; magnetic resonance imaging; multiple sclerosis; tomography

DESCRIPTION (Adapted from Applicant's Abstract): The broad long-term goal of this project was to develop theories, algorithms, and their portable computer implementations for quantifying object information captured in multidimensional images and to apply them to solve specific biomedical problems. The lack of cost-effective methods with proven precision and accuracy for extracting object information from images remains one of the major impediments in many radiological applications. With this in mind, the applicants proposed: (1) to advance the theory, algorithms, and their efficient computer implementations for detecting and delineating objects in multidimensional, multiparametric images in general, and (2) to apply these methods to the problem of quantifying MS lesions of the brain via MR imagery. The methods proposed in this application are based on a theory relating to definition of objects in images. Since images are by nature fuzzy, the theory considers objects as a set of image elements that "hang-together" fuzzily. A fuzzy topological concept called fuzzy connectedness is introduced that captures the idea of "hanging-togetherness." Although the concept is computationally impractical, key theoretical results lead to practical computer algorithms for detecting fuzzy objects in images. MS is an intensively studied disease of the nervous system. Its detection and quantification via MR images has proved critical to the monitoring of this disease and of its response to therapy. However, a practical solution to this problem is still not available. Supported by strong preliminary results, this application proposes practical methods based on fuzzy connectedness for the detection and quantification of MS lesions. The overall hypothesis underlying this research is that the methods resulting from this investigation will be practical, cost-effective, and reliable for quantifying MS lesions with a precision and accuracy that is acceptable for conducting clinical trials.

描述(改编自申请者摘要)：宏大的长期目标 这个项目的目的是开发理论、算法和它们的可移植 用于量化在中捕获的对象信息的计算机实现 多维图像及其在解决特定生物医学中的应用 有问题。缺乏经过验证的精确度和成本效益高的方法 从图像中提取对象信息的精度仍然是 在许多放射学应用中的主要障碍。考虑到这一点， 申请者提出：(1)推进理论、算法和他们的 用于检测和描绘对象的高效计算机实现 一般的多维、多参数图像，以及(2)应用这些 磁共振对多发性硬化症脑内病变进行定量化的方法 想象。本申请中提出的方法是基于一种理论 与图像中的对象的清晰度有关。因为图像本质上是 模糊，该理论认为物体是一组图像元素， 含糊其辞地“团结在一起”。一种称为模糊的模糊拓扑概念 连接性的引入抓住了 “团结在一起。”尽管这个概念在计算上是 不切实际的关键理论结果导致了实用的计算机算法 用于检测图像中的模糊对象。多发性硬化症是一种被广泛研究的疾病。 神经系统的一部分。通过磁共振图像对其进行检测和量化 事实证明，这对监测这种疾病及其对 心理治疗。然而，这个问题的实际解决方案仍然不是 可用。在强劲的初步结果的支持下，这项申请 提出了基于模糊连通度的实用检测方法 并对MS病变进行量化。这背后的总体假设是 研究表明，这次调查得出的方法将是 实用、经济有效且可靠的MS病变量化方法 临床试验可接受的精密度和准确度。