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

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Abstract

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.

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Authors and Affiliations

  1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China

    Juanjuan Zhao, Guohua Ji, Yan Qiang & Xiaolei Liao

  2. Key Laboratory of Advanced Transducers and Intelligent Control Systems, Taiyuan University of Technology, Taiyuan, 030024, China

    Xiaohong Han

Authors
  1. Juanjuan Zhao
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  2. Guohua Ji
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  3. Xiaohong Han
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  4. Yan Qiang
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  5. Xiaolei Liao
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Corresponding author

Correspondence to Juanjuan Zhao.

Additional information

Juanjuan Zhao is an associate professor in the School of Computer Science and Technology at Taiyuan University of Technology (TYUT), China. She received her PhD in computer application technology from TYUT in 2010. Her current research interests are medical image processing and the Internet of Things.

Guohua Ji received her BS in Computer Science and Technology from the China University of Mining and Technology, China in 2013. She is currently pursuing her MD in the area of image processing at Taiyuan University of Technology, China.

Xiaohong Han received her PhD in Computer Science from Taiyuan University of Technology (TYUT), China in 2013. She is currently a lecturer at the Key Laboratory of Advanced Transducers and Intelligent Control Systems in TYUT. Her research interests are optimization, chaotic signal processing, noise reduction, digital filtering and feature selection.

Yan Qiang received his PhD in Computer Application Technology from Taiyuan University of Technology (TYUT), China in 2010. He is a professor at the School of Computer Science and Technology at TYUT. His current research topics include image processing and cloud computing.

Xiaolei Liao received his BS in Computer Science and Technology from the Taiyuan University of Technology (TYUT), China in 2014. He is currently pursuing his MS in the area of medical image processing at TYUT.

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Zhao, J., Ji, G., Han, X. et al. An automated pulmonary parenchyma segmentation method based on an improved region growing algorithmin PET-CT imaging. Front. Comput. Sci. 10, 189–200 (2016). https://doi.org/10.1007/s11704-015-4543-x

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  • DOI: https://doi.org/10.1007/s11704-015-4543-x

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