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A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm

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  • Juanjuan Zhao
  • Guohua Ji
  • Yan Qiang
  • Xiaohong Han
  • Bo Pei
  • Zhenghao Shi

Abstract

Background: Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives. Method: Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method. Results: Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).

Suggested Citation

  • Juanjuan Zhao & Guohua Ji & Yan Qiang & Xiaohong Han & Bo Pei & Zhenghao Shi, 2015. "A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    • Handle: RePEc:plo:pone00:0123694
    DOI: 10.1371/journal.pone.0123694
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    Cited by:

    1. Guilherme Giacomini & Ana Luiza Menegatti Pavan & João Mauricio Carrasco Altemani & Sergio Barbosa Duarte & Carlos Magno Castelo Branco Fortaleza & José Ricardo de Arruda Miranda & Diana Rodrigues de , 2018. "Computed tomography-based volumetric tool for standardized measurement of the maxillary sinus," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-12, January.
    2. Xiaolei Liao & Juanjuan Zhao & Cheng Jiao & Lei Lei & Yan Qiang & Qiang Cui, 2016. "A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.

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