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A Fully Automatic Method for Lung Parenchyma Segmentation and Repairing

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Abstract

Considering that the traditional lung segmentation algorithms are not adaptive for the situations that most of the juxtapleural nodules, which are excluded as fat, and lung are not segmented perfectly. In this paper, several methods are comprehensively utilized including optimal iterative threshold, three-dimensional connectivity labeling, three-dimensional region growing for the initial segmentation of the lung parenchyma, based on improved chain code, and Bresenham algorithms to repair the lung parenchyma. The paper thus proposes a fully automatic method for lung parenchyma segmentation and repairing. Ninety-seven lung nodule thoracic computed tomography scans and 25 juxtapleural nodule scans are used to test the proposed method and compare with the most-cited rolling-ball method. Experimental results show that the algorithm can segment lung parenchyma region automatically and accurately. The sensitivity of juxtapleural nodule inclusion is 100 %, the segmentation accuracy of juxtapleural nodule regions is 98.6 %, segmentation accuracy of lung parenchyma is more than 95.2 %, and the average segmentation time is 0.67 s/frame. The algorithm can achieve good results for lung parenchyma segmentation and repairing in various cases that nodules/tumors adhere to lung wall.

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Acknowledgments

This work is supported by the National Nature Science Foundation of China (grant no. 60671050) and Ministry of Higher Education through Fundamental Research Grant Scheme for the Central Universities (grant no. N100404010).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, No. 3 Wenhua Road, P.O. Box 128, Shenyang, 110004, People’s Republic of China

    Ying Wei & Juan-juan Li

  2. Key Laboratory of Medical Image Computing (Northeastern University), Ministry of Education, Shenyang, 110004, China

    Ying Wei

  3. Hunnan New District, Neusoft Medical Systems Co., Ltd, No. 16 Century Road, Shenyang, 110179, People’s Republic of China

    Guo Shen

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  1. Ying Wei
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  2. Guo Shen
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  3. Juan-juan Li
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Correspondence to Ying Wei.

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Wei, Y., Shen, G. & Li, Jj. A Fully Automatic Method for Lung Parenchyma Segmentation and Repairing. J Digit Imaging 26, 483–495 (2013). https://doi.org/10.1007/s10278-012-9528-9

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  • DOI: https://doi.org/10.1007/s10278-012-9528-9

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