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Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules.

Methods

A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule’s potential malignancy on the 10 most similar cases and by the parameters of precision and recall.

Results

Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval.

Conclusion

Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

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Acknowledgments

This study was funded by Fundação de Amparo à Pesquisa do Estado de Alagoas (Grant Number 20130603-002-0040-0063) with a master scholarship to José Raniery Ferreira Junior.

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Authors and Affiliations

  1. Center of Imaging Sciences and Medical Physics, Internal Medicine Department, Ribeirao Preto Medical School, University of Sao Paulo (USP), Av. dos Bandeirantes, 3900, Campus USP, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil

    José Raniery Ferreira Jr. & Paulo Mazzoncini de Azevedo-Marques

  2. Lab of Telemedicine and Medical Informatics, University Hospital Prof. Alberto Antunes, Institute of Computing, Federal University of Alagoas (UFAL), Av. Lourival Melo Mota, s/n, Campus A. C. Simões, Cidade Universitária, Maceió, Alagoas, 57072-900, Brazil

    Marcelo Costa Oliveira

Authors
  1. José Raniery Ferreira Jr.
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  2. Paulo Mazzoncini de Azevedo-Marques
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  3. Marcelo Costa Oliveira
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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, 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., de Azevedo-Marques, P.M. & Oliveira, M.C. Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval. Int J CARS 12, 509–517 (2017). https://doi.org/10.1007/s11548-016-1471-7

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  • DOI: https://doi.org/10.1007/s11548-016-1471-7

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