An automated pulmonary parenchyma segmentation method based on an improved region growing algorithmin PET-CT imaging

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• 作者：Juanjuan Zhao ; Guohua Ji ; Xiaohong Han ; Yan Qiang…
• 关键词：pulmonary parenchyma segmentation ; bottom region of lung ; image binarization ; iterative threshold ; seeded region growing ; four ; corner rotating and scanning ; denoising ; contour refining ; PET ; CT
• 刊名：Frontiers of Computer Science in China
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：10
• 期：1
• 页码：189-200
• 全文大小：698 KB
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• 作者单位：Juanjuan Zhao (1)
  Guohua Ji (1)
  Xiaohong Han (2)
  Yan Qiang (1)
  Xiaolei Liao (1)
  
  1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
  2. Key Laboratory of Advanced Transducers and Intelligent Control Systems, Taiyuan University of Technology, Taiyuan, 030024, China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight-neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this paper. The proposed method consists of four main stages: image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, middle, and bottom region of lung. Finally, corrosion and expansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emission tomography-computed tomography (PET-CT) data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and accurately. Keywords pulmonary parenchyma segmentation bottom region of lung image binarization iterative threshold seeded region growing four-corner rotating and scanning denoising contour refining PET-CT