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Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays

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

Abstract

Purpose

To improve detection of pulmonary and pleural abnormalities caused by pneumonia or tuberculosis (TB) in digital chest X-rays (CXRs).

Methods

A method was developed and tested by combining shape and texture features to classify CXRs into two categories: TB and non-TB cases. Based on observation that radiologist interpretation is typically comparative: between left and right lung fields, the algorithm uses shape features to describe the overall geometrical characteristics of the lung fields and texture features to represent image characteristics inside them.

Results

Our algorithm was evaluated on two different datasets containing tuberculosis and pneumonia cases.

Conclusions

Using our proposed algorithm, we were able to increase the overall performance, measured as area under the (ROC) curve (AUC) by 2.4 % over our previous work.

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Notes

  1. Firefly was developed by University of Missouri, and it is an online annotating toolbox.

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Author information

Authors and Affiliations

  1. Communications Engineering Branch, Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Alexandros Karargyris, Stefan Jaeger, Sema Candemir, Zhiyun Xue, K. C. Santosh, Szilárd Vajda, Sameer Antani & George R. Thoma

  2. Division of Emergency Radiology, Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA

    Jenifer Siegelman

  3. Center for Evidence Based Imaging, Harvard Medical School, Boston, MA, USA

    Jenifer Siegelman

  4. Ugeianet Diagnostic Center, General Hospital of Athens KAT, Athens, Greece

    Dimitris Tzortzis

  5. Radiology Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA

    Les Folio

Authors
  1. Alexandros Karargyris
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  2. Jenifer Siegelman
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  3. Dimitris Tzortzis
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  4. Stefan Jaeger
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  5. Sema Candemir
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  6. Zhiyun Xue
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  7. K. C. Santosh
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  8. Szilárd Vajda
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  9. Sameer Antani
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  10. Les Folio
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  11. George R. Thoma
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Corresponding author

Correspondence to Alexandros Karargyris.

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Conflict of interest

Alexandros Karargyris, Jenifer Siegelman, Dimitris Tzortzis, Stefan Jaeger, Sema Candemir, Zhiyun Xue, KC Santosh, Szilárd Vajda, Sameer Antani, Les Folio and George R. Thoma declare that they have no conflict of interest.

Appendix

Appendix

There are two datasets that are used in this paper:

  1. 1.

    Shenzhen dataset [11]: It was acquired from Shenzhen Hospital in China. It has a good variety of TB cases. They were captured over a month period as part of the daily routine at Shenzhen Hospital, using a Philips DR Digital Diagnost system. The set contains 340 normal CXRs and 275 abnormal CXRs with TB along with radiologist readings. The dataset is publicly available here: http://archive.nlm.nih.gov/repos/chestImages.php.

  2. 2.

    JSRT dataset [15]: This is a popular publicly available dataset from the Japanese Society of Radiological Technology (JSRT). This dataset contains 154 nodule and 93 non-nodule CXRs along with manual annotations of the lung fields.

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Karargyris, A., Siegelman, J., Tzortzis, D. et al. Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays. Int J CARS 11, 99–106 (2016). https://doi.org/10.1007/s11548-015-1242-x

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  • DOI: https://doi.org/10.1007/s11548-015-1242-x

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