Research on the Automatic Segmentation of the Thoracic CT DICOM Images

Deng Jie-hang; Lü Zhuo-rong; Xiao Can-hui

doi:10.3969/j.issn.1007-7162.2013.02.008

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Research on the Automatic Segmentation of the Thoracic CT DICOM Images

更新时间：2026-05-27

- Research on the Automatic Segmentation of the Thoracic CT DICOM Images
- Vol. 30, Issue 2, Pages: 37-41(2013)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1007-7162.2013.02.008
  CLC：
- Online First：27 June 2013，
  
  Published：2013
- 稿件说明：
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邓杰航, 吕灼荣, 肖灿辉. 胸部CT DICOM图像自动分割的研究与实现[J]. 广东工业大学学报, 2013, 30(2): 37-41.

Deng Jie-hang, Lü Zhuo-rong, Xiao Can-hui. Research on the Automatic Segmentation of the Thoracic CT DICOM Images[J]. 2013, 30(2): 37-41.
邓杰航, 吕灼荣, 肖灿辉. 胸部CT DICOM图像自动分割的研究与实现[J]. 广东工业大学学报, 2013, 30(2): 37-41. DOI： 10.3969/j.issn.1007-7162.2013.02.008.

Deng Jie-hang, Lü Zhuo-rong, Xiao Can-hui. Research on the Automatic Segmentation of the Thoracic CT DICOM Images[J]. 2013, 30(2): 37-41. DOI： 10.3969/j.issn.1007-7162.2013.02.008.

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.

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.

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