中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (26): 4823-4826.doi: 10.3969/j.issn.1673-8225.2010.26.018

• 骨与关节图像与影像 bone and joint imaging • 上一篇    下一篇

一种MR图像灰度偏差场的修正方法

周  震，刘春红   

  1. 首都医科大学，北京市  100069
  • 出版日期:2010-06-25 发布日期:2010-06-25
  • 作者简介:周 震，主要从事影像医学与核医学方面的研究。 zhouzhenbme@gmail.com
  • 基金资助:

    首都医科大学基础-临床科研合作基金(10JL63)。

Bias field correction of magnetic resonance imaging

Zhou Zhen, Liu Chun-hong   

  1. Capital Medical University, Beijing  100069, China
  • Online:2010-06-25 Published:2010-06-25
  • About author:Zhou Zhen, Capital Medical University, Beijing 100069, China zhouzhenbme@ gmail.com
  • Supported by:

    the Basic Clinical Scientific Research Program of Capital Medical University, No. 10JL63*

PDF

490

被引次数

5

摘要：

背景：目前的核磁共振图像不可避免会有灰度偏差场存在，会对医学图像的计算机数字处理产生非常不利的影响，如分割、配准、量化等。因此对于这种不利的空间密度变化的修正成为许多图像分析任务必要的预处理步骤。
目的：提出一种以图像数据为基础不依赖于模板的复杂图像信号偏差场的修正方法。
方法：数据是来源于首都医科大学复兴医院放射影像科，采集于美国GE公司核磁共振设备，所有图像来源于志愿者的核磁共振图像。成像视野250 mm×250 mm，矩阵256×256，层厚1 mm。对核磁共振图像处理目标进行锁定的Mask方法，并借助信息熵的方法实现对核磁共振图像灰度偏差场的有效修正。
结果与结论：通过核磁共振图像实验，证明该方法可以有效准确地修正核磁共振图像灰度偏差场的伪影。实验结果为核磁共振图像计算机分析提供了一种有效的修正灰度偏差场方法，提高了图像分析的准确性和鲁棒性。

关键词: 信息论, MRI, 熵, 同态滤波, 偏差场

Abstract:

BACKGROUND: Current magnetic resonance (MR) imaging has gray bias field, which affects digital processing of medical images, such as segmentation, registration, and quantization. Correction of this adverse space density changes is a necessary process before imaging analysis.
OBJECTIVE: To propose a model independent correction method of complex image signal bias field based on image data.
METHODS: Data were provided by the Department of Radiology, Fuxing Hospital of Capital Medical University, and collected by MR device (GE, USA). All MR images were provided by the volunteers (imaging visual field 250 mm×250 mm, matrix 256×256, slice thickness 1 mm). Based on entropy method and Mask method, MR image bias field was corrected. 
RESULTS AND CONCLUSION: This method can correct MR image bias field accurately and effectively. These results provided a new method of MR image bias field correction for MR image analysis, which improved the image analysis accuracy and robustness.

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