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A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection

International Journal of Computer Assisted Radiology and Surgery volume 13, pages 397–409 (2018)Cite this article

Abstract

Purpose

This work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes.

Methods

The 3D region of each detected nodule candidate is first reconstructed using the sparse field method for accurately segmenting the objects. This technique enhances the level set modeling by restricting the computations to a narrow band near the evolving curve. Then, a set of 2D and 3D relevant features are extracted for each segmented candidate. Subsequently, a hybrid undersampling/boosting algorithm called RUSBoost is applied to analyze the features and discriminate real nodules from non-nodules.

Results

The performance of the proposed scheme was evaluated by using 70 CT images, randomly selected from the Lung Image Database Consortium and containing 198 nodules. Applying RUSBoost classifier exhibited a better performance than some commonly used classifiers. It effectively reduced the average number of FPs to only 3.9 per scan based on a fivefold cross-validation.

Conclusion

The practical implementation, applicability for different nodule types and adaptability in handling the imbalanced data classification insure the improvement in lung nodules detection by utilizing this new approach.

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Acknowledgements

The authors would like to thank Dr. Mahsa Fathian and Dr. Roya Fallahian for their clinical guidance and valuable comments.

Author information

Affiliations

  1. Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran

    Soudeh Saien

  2. Department of Electrical Engineering, K.N.Toosi University of Technology, Tehran, Iran

    Hamid Abrishami Moghaddam

  3. Department of Computer Engineering and Information Technology, Hamedan University of Technology, Hamedan, Iran

    Mohsen Fathian

Authors
  1. Soudeh Saien
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  2. Hamid Abrishami Moghaddam
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  3. Mohsen Fathian
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Correspondence to Soudeh Saien.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Cite this article

Saien, S., Moghaddam, H.A. & Fathian, M. A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection. Int J CARS 13, 397–409 (2018). https://doi.org/10.1007/s11548-017-1656-8

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  • DOI: https://doi.org/10.1007/s11548-017-1656-8

Keywords

  • Lung nodules detection
  • False positives reduction
  • Imbalanced data classification
  • Sparse field method
  • RUSBoost classifier