广东工业大学学报 ›› 2013, Vol. 30 ›› Issue (2): 37-41.doi: 10.3969/j.issn.1007-7162.2013.02.008

• 综合研究 • 上一篇    下一篇

胸部CT DICOM图像自动分割的研究与实现

邓杰航1,吕灼荣2,肖灿辉3   

  1. 1.广东工业大学 计算机学院, 广东 广州  510006;2.从化中心医院 内科, 广东 广州  510900;3.从化中心医院 传染科, 广东 广州  510900
  • 收稿日期:2013-01-05 出版日期:2013-06-27 发布日期:2013-06-27
  • 作者简介:邓杰航(1979-),男,讲师,博士,主要研究方向为医学图像处理.
  • 基金资助:

    国家自然科学基金资助项目(61202267); 广东省自然科学基金资助项目(S2011040004295)

Research on the Automatic Segmentation of the Thoracic CT DICOM Images

Deng Jie-hang1, Lü Zhuo-rong2, Xiao Can-hui3   

  1. 1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China; 2. Department of Internal Medicine, Conghua Central Hospital, Guangzhou 510900, China; 3. Department of Infectious Disease, Conghua Central Hospital, Guangzhou 510900, China
  • Received:2013-01-05 Online:2013-06-27 Published:2013-06-27

摘要: 针对胸部CT (Computed Tomography) DICOM(Digital Imaging and Communication of Medicine) 图像中多窗显示时,需要对胸部各组织进行分割的问题,本文根据各组织的CT值范围,结合贴标签算法实现多组织分割.首先根据CT成像原理,不同组织具有稳定的CT值范围,将图像初步分割为肺、肌肉、骨和皮肤区域.然后依据组织的面积特征,应用贴标签算法去除噪声.不同厂家成像系统,不同年龄、性别患者图像的分割结果表明,本文方法都能正确分割出肺、肌肉、骨和皮肤区域.

关键词: 胸部CT; 多窗显示; 面积; 分割; 去噪

Abstract: For the problem that the thoracic CT (Computed tomography) DICOM (Digital Imaging and Communication of Medicine) images need to be segmented to show different tissues clearly in the multiwindow mode, these images were segmented according to the CT value range by connectedcomponent labeling. Firstly, the thoracic images were segmented into lung, muscle, bone and skin regions with stable CT value range, based on the CT imaging theory. The noise in the segmented regions was removed by the connected-component labeling algorithm according to the area of the objects. The thoracic CT images were created in different imaging systems from different patients of various ages and genders. Experimental results show that the lung, muscle, bone and skin regions can be segmented correctly in different data sets via the proposed method.

Key words: thoracic CT; multi-window; area; segmentation; noise removal

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