OBJECT DEFINITION IN TOMOGRAPHIC RADIOLOGY

描述(由申请人提供)：该项目的总体长期目标 是开发理论、算法及其实际的计算机实现 识别和量化在多维空间中捕获的对象信息 医学图像。在许多放射学应用中，缺乏成本效益 具有经过验证的、可接受的精密度和准确度的用于此目的的方法 这是进一步取得进展的主要障碍之一。考虑到这一点，这一点 提案涉及三个目标：(I)标准化MR图像强度等级 从而促进物体定义和组织表征； 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)设计新的基于磁共振图像的定量测量方法， 将为多发性硬化症(MS)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 变形)，而不是在幻影上，因此它将适用于 已经获取的图像也是如此。组织区域的分割是通过 将模糊连通性方法推广到多个对象，其中 考虑对象之间的相对连通性来描述对象的模糊性 接口。量化多发性硬化症严重程度的新措施是基于 标准化图像强度在组织区域内的分布。 多发性硬化症是一种后天的中枢神经系统疾病，它给美国带来的代价是 估计每年25亿美元。精准、准确、经济实惠的 目前缺乏基于图像的确定疾病严重程度的方法。 提出的方法不仅有助于理解多发性硬化症及其进展 而且在确定各种疗法的有效性方面也是如此。

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.