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Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter

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

Abstract

Purpose

Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations.

Methods

Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters.

Results

The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80 % of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4–36 times faster than existing methods.

Conclusion

Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images.

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Abbreviations

CAD:

Computer-aided detection

CNEF:

Cylindrical nodule-enhancement filter

C-SVC:

C-support vector classification

CT:

Computed tomography

FROC:

Free-response receiver operating characteristic

GGO:

Ground glass opacity

LIDC:

Lung image database consortium

MIP:

Maximum intensity projection

PET:

Positron emission tomography

SVM:

Support vector machine

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Authors and Affiliations

  1. Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake-city, Aichi , 470-1192, Japan

    Atsushi Teramoto

  2. Department of Intelligent Image Information, Graduate School of Medicine Gifu University, 1-1 Yanagido, Gifu-city, Gifu, 501-1194, Japan

    Hiroshi Fujita

Authors
  1. Atsushi Teramoto
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  2. Hiroshi Fujita
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Corresponding author

Correspondence to Atsushi Teramoto.

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Teramoto, A., Fujita, H. Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J CARS 8, 193–205 (2013). https://doi.org/10.1007/s11548-012-0767-5

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  • DOI: https://doi.org/10.1007/s11548-012-0767-5

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