Suppr超能文献

一种用于在头部的CT和MR容积图像中查找并定位外部附着标记物的自动技术。

An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head.

作者信息

Wang M Y, Maurer C R, Fitzpatrick J M, Maciunas R J

机构信息

Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.

出版信息

IEEE Trans Biomed Eng. 1996 Jun;43(6):627-37. doi: 10.1109/10.495282.

Abstract

An image processing technique is presented for finding and localizing the centroids of cylindrical markers externally attached to the human head in computed tomography (CT) and magnetic resonance (MR) image volumes. The centroids can be used as control points for image registration. The technique, which is fast, automatic, and knowledge-based, has two major steps. First, it searches the entire image volume to find one voxel inside each marker-like object. We call this voxel a "candidate" voxel, and we call the object a candidate marker. Second, it classifies the voxels in a region surrounding the candidate voxel as marker or nonmarker voxels using knowledge-based rules and calculates an intensity-weighted centroid for each true marker. We call this final centroid the "fiducial" point of the marker. The technique was developed on 42 scans of six patients-one CT and six MR scans per patient. There are four markers attached to each patient for a total of 168 marker images. For the CT images the false marker rate was zero. For MR the false marker rate was 1.4% (Two out of 144 markers). To evaluate the accuracy of the fiducial points, CT-MR registration was performed after correcting the MR images for geometrical distortion. The fiducial registration accuracy averaged 0.4 mm and was better than 0.6 mm for each of the eighteen image pairs.

摘要

本文提出了一种图像处理技术,用于在计算机断层扫描(CT)和磁共振(MR)图像体积中查找并定位外部附着在人头部的圆柱形标记的质心。这些质心可作为图像配准的控制点。该技术快速、自动且基于知识,有两个主要步骤。首先,它在整个图像体积中搜索,以在每个类似标记的物体内部找到一个体素。我们将这个体素称为“候选”体素,将该物体称为候选标记。其次,它使用基于知识的规则将候选体素周围区域中的体素分类为标记或非标记体素,并为每个真实标记计算强度加权质心。我们将这个最终质心称为标记的“基准”点。该技术是在对六名患者进行的42次扫描上开发的——每位患者一次CT扫描和六次MR扫描。每位患者附着四个标记,总共168个标记图像。对于CT图像,假标记率为零。对于MR图像,假标记率为1.4%(144个标记中有两个)。为了评估基准点的准确性,在对MR图像进行几何失真校正后进行CT-MR配准。基准配准精度平均为0.4毫米,对于18对图像中的每一对都优于0.6毫米。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验