OBJECT DEFINITION IN TOMOGRAPHIC RADIOLOGY

描述（改编自申请人摘要）：广泛的长期目标 这个项目的目的是开发理论，算法，以及它们的便携式 用于量化在图像中捕获的对象信息的计算机实现 多维图像，并将其应用于解决特定的生物医学 问题 缺乏具有成本效益和经证明的精确度的方法， 从图像中提取对象信息的准确性仍然是 这是许多放射性应用中的主要障碍。 考虑到这一点， 申请人提出：（1）推进理论，算法，及其 用于检测和描绘物体的有效计算机实现 多维，多参数图像一般，和（2）应用这些 通过MR量化脑部MS病变的问题的方法 意象 本申请中提出的方法基于一种理论， 涉及图像中对象的定义。 因为图像本质上 模糊，该理论认为对象是一组图像元素， 模糊地“挂在一起” 一个模糊的拓扑概念叫做fuzzy 引入了连通性，它抓住了 “挂在一起。“虽然这个概念在计算上 不切实际的，关键的理论结果导致实际的计算机算法 用于检测图像中的模糊对象。 MS是一种深入研究的疾病 的神经系统。 通过MR图像对其进行检测和量化， 对监测这一疾病及其应对措施至关重要， 疗法 然而，这个问题的实际解决方案仍然没有 available. 在强大的初步结果的支持下，该应用程序 提出了基于模糊连通性的实用检测方法 和MS病变的定量。 这背后的总体假设是 研究是，从这项调查产生的方法将是 用于量化MS病变的实用、成本效益高且可靠， 进行临床试验时可接受的精密度和准确度。

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.