OBJECT DEFINITION IN TOMOGRAPHIC RADIOLOGY

描述（由申请人提供）：该项目的广泛的长期目标 是发展理论，算法和它们的实际计算机实现 识别和量化在多维空间中捕获的对象信息， 医学图像在许多放射性应用中，缺乏成本效益 用于此目的的方法具有经证实的、可接受的精密度和准确度， 进一步发展的主要障碍之一。考虑到这一点，这 该提案提出了三个目标：（i）标准化MR图像强度标度 从而便于对象定义和组织表征;（ii） 将大脑中的组织区域分割成解剖区域和病变区域， 组织区域;（iii）设计新的基于MR图像的定量测量， 将在多发性硬化症（MS）中提供更多疾病特异性信息。 标准化方法完全基于图像处理（直方图 而不是在幻影上，因此它将适用于 已经获得的图像。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， 考虑对象之间的相对连通性， 接口。量化MS疾病严重程度的新措施是基于 组织区域内的标准化图像强度分布。 MS是一种获得性中枢神经系统疾病，其在美国的成本为 估计每年25亿美元。一种精确、准确、高性价比的 目前缺乏确定疾病严重程度的基于图像的方法。 所提出的方法不仅有助于理解MS及其进展 而且还可以确定各种疗法的有效性。

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.