首都医科大学学报 ›› 2008, Vol. 29 ›› Issue (3): 332-335.

• 基础研究 • 上一篇    下一篇

人脑磁共振图像中海马结构的计算机辅助分割新方法

孟薇1,2, 叶德荣2   

  1. 1. 首都医科大学生物医学工程学院;2. 中国医学科学院北京协和医院放射科
  • 收稿日期:2007-11-16 修回日期:1900-01-01 出版日期:2008-06-24 发布日期:2008-06-24
  • 通讯作者: 叶德荣

A New Approach to Computer-aided Segmentation of Hippocampal Formation in Human Brain MR Images

Meng Wei1,2, Ye Derong2   

  1. 1. School of Biomedical Engineering, Capital Medical University;2. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences
  • Received:2007-11-16 Revised:1900-01-01 Online:2008-06-24 Published:2008-06-24

摘要: 目的 为了降低手工分割MR图像中海马结构的难度,促进相关研究及应用,本文提出了一种计算机辅助分割海马结构的新方法.方法 对感兴趣区域应用抗噪边缘检测方法以阈值为零提取所有可能的边界并加亮显示;通过标记区域对角点,恢复非目标边缘区域的像素亮度;对照原图像应用计算机辅助工具进一步完善海马结构的边界,通过调整边界像素点实现精确完整地分割.结果 提高了手工分割海马结构的可重复性,降低了工作强度,同时可以达到手工分割的精度.结论 尽管本研究方法仍然无法完全摆脱手工处理,但是其操作简单、实用.在自动分割精度尚不能满足临床应用要求的条件下,不失为一种可行的替代方法.

关键词: 图像分割, 磁共振图像, 海马结构

Abstract: Objective To overcome the difficulties of delineating hippocampal boundaries in an effective way and benefit the related researches and applications by quantitative information from MR images,the paper presents a new method of computer-aided segmentation.Methods All possible edges at the region of interest were extracted by de-nosing edge detection methods with threshold zero,and they were highlighted.Compared with the original image,the edges which belong to the boundaries of hippocampus were identified and others were removed by a tool.The tool was the computer program developed for aiding to remove edges and modified the boundaries of hippocampal formation.Multiple edges could be removed by marking the two diagonal points of the region at which edges located.With the help of the tool,the magnified boundaries could be further improved by adding the missed pixels and deleting the redundant points,which guarantee the accuracy of the boundaries.Results Most parts of hippocampal boundaries could be well detected by Sobel operators.This made the proposed method more reproducible than manual segmentation.It took lower cost to remove undesired edges by the tool than to delineate hippocampal boundaries by manual tracing.With the guide of the boundaries detected,it was easier to locate the missing reqions of boundaries.Adjusting few pixels at boundaries made it possible to achieve the same accuracy as manual segmentation.Conclusion Although the proposed method cannot free us from manual operation,it is simple and practical.Before reliable automated approaches are fully available,the technique may be a better alternative.

Key words: image segmentation, MR image, hippocampal formation

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