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Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs

  • Systems-Level Quality Improvement
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Abstract

To detect pulmonary abnormalities such as Tuberculosis (TB), an automatic analysis and classification of chest radiographs can be used as a reliable alternative to more sophisticated and technologically demanding methods (e.g. culture or sputum smear analysis). In target areas like Kenya TB is highly prevalent and often co-occurring with HIV combined with low resources and limited medical assistance. In these regions an automatic screening system can provide a cost-effective solution for a large rural population. Our completely automatic TB screening system is processing the incoming CXRs (chest X-ray) by applying image preprocessing techniques to enhance the image quality followed by an adaptive segmentation based on model selection. The delineated lung regions are described by a multitude of image features. These characteristics are than optimized by a feature selection strategy to provide the best description for the classifier, which will later decide if the analyzed image is normal or abnormal. Our goal is to find the optimal feature set from a larger pool of generic image features, –used originally for problems such as object detection, image retrieval, etc. For performance evaluation measures such as under the curve (AUC) and accuracy (ACC) were considered. Using a neural network classifier on two publicly available data collections, –namely the Montgomery and the Shenzhen dataset, we achieved the maximum area under the curve and accuracy of 0.99 and 97.03%, respectively. Further, we compared our results with existing state-of-the-art systems and to radiologists’ decision.

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  1. http://www.mathworks.com/help/images/ref/regionprops.html

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Acknowledgments

This research is supported in past by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine, and Lister Hill National Center for Biomedical Communications (LHNCBC).

The authors are grateful to Mr. Rodney Long for the fruitful discussions during the development of this project.

Funding

This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM), and Lister Hill National Center for Biomedical Communications (LHNCBC).

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Authors and Affiliations

  1. Central Washington University, Ellensburg, WA, USA

    Szilárd Vajda

  2. IBM Almaden Research, San Jose, CA, USA

    Alexandros Karargyris

  3. University of South Dakota, Vermillion, SD, USA

    K.C. Santosh

  4. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Stefan Jaeger, Sema Candemir, Zhiyun Xue, Sameer Antani & George Thoma

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  1. Szilárd Vajda
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  2. Alexandros Karargyris
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  3. Stefan Jaeger
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  4. K.C. Santosh
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  5. Sema Candemir
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  6. Zhiyun Xue
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Correspondence to Szilárd Vajda.

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Authors declare that they have no conflict of interest.

Ethical approval

All images used in this study were collected prior to this study during routine clinical care. They were de-identified at source and have been exempted from review (NIH IRB# 5357).

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https://ceb.nlm.nih.gov/repos/chestImages.php

This article is part of the Topical Collection on Advanced Computational Intelligence and Soft Computing in Medical Imaging

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Vajda, S., Karargyris, A., Jaeger, S. et al. Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs. J Med Syst 42, 146 (2018). https://doi.org/10.1007/s10916-018-0991-9

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