A Model Based System For The Interpretation Of MR Human Brain Scans
A Model Based System For The Interpretation Of MR Human Brain Scans
复制标题
用于解释 MR 人脑扫描的基于模型的系统
DOI：
10.1117/12.968663
复制
发表时间：
1988
影响因子：
23.6
通讯作者：
C. Kulikowski
中科院分区：
文献类型：
--
作者：
I. Kapouleas;C. Kulikowski
本文描述了一个原型系统，用于识别和表征多发性硬化症（MS）病变的大脑磁共振（MR）图像。该系统的目的是获得一个初始分割的每一个横截面图像与低层次的视觉方法，然后通过模型驱动的方法，相关的信息从T1和T2图像和3-D信息互补的横截面时，得到连续细化的图像子区域。该系统使用与个人大脑特征相匹配的大脑b样条曲面模型。然后，在执行连续的细化操作以检测MS病变之前，按比例缩放大脑的正常内部结构。该系统的低水平视觉和实体建模组件已在来自许多MR患者研究的数百张图像上成功测试。模型拟合的第一步已经实施，并显示出可喜的成果。
This paper describes a prototype system for identifying and characterizing Multiple Sclerosis (MS) lesions in the brain from magnetic resonance (MR) images. The system is designed to obtain an initial segmentation of each cross-sectional image with low level vision methods, and then derive successive refinements of image subregions through a model-driven approach that correlates relevant information from T1 and T2 images and 3-D information from complementary cross-sections when necessary. The system uses a b-spline surface model of the brain that matches the characteristics of the individual's brain. The normal internal structures of the brain are then scaled proportionately before carrying out the successive refinement operations for the detection of the MS lesions. The low level vision and the solid modeling components of the system have been successfully tested on several hundred images from a number of MR patient studies. The first steps of model fitting have been implemented and show promising results.