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Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture

Journal of Digital Imaging volume 31, pages 451–463 (2018)Cite this article

Abstract

Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.

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Funding

This study was funded by Fundação de Amparo à Pesquisa do Estado de Alagoas and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (grant no. 20130603-002-0040-0063)

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Affiliations

  1. Center of Imaging Sciences and Medical Physics, Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil

    José Raniery Ferreira Jr & Paulo Mazzoncini de Azevedo-Marques

  2. Institute of Computing, Federal University of Alagoas, Av. Lourival Melo Mota, Cidade Universitária, Maceió, Alagoas, 57072-900, Brazil

    Marcelo Costa Oliveira

Authors
  1. José Raniery Ferreira Jr
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  2. Marcelo Costa Oliveira
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  3. Paulo Mazzoncini de Azevedo-Marques
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Corresponding author

Correspondence to José Raniery Ferreira Jr.

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The authors declare that they have no conflict of interest.

Ethical approval

For this type of study formal consent is not required. This study used a public image database (https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX), which all protected health information (PHI) contained within the DICOM headers of the images were removed in accordance with Health Insurance Portability and Accountability Act (HIPAA) guidelines.

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Ferreira, J.R., Oliveira, M.C. & de Azevedo-Marques, P.M. Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture. J Digit Imaging 31, 451–463 (2018). https://doi.org/10.1007/s10278-017-0029-8

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  • DOI: https://doi.org/10.1007/s10278-017-0029-8

Keywords

  • Lung cancer
  • Pulmonary nodule
  • Image classification
  • Pattern recognition