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Oda, H.* ; Bhatia, K.K.* ; Oda, M.* ; Kitasaka, T.* ; Iwano, S.* ; Homma, H.* ; Takabatake, H.* ; Mori, M.* ; Natori, H.* ; Schnabel, J.A.* ; Mori, K.*

Automated mediastinal lymph node detection from CT volumes based on intensity targeted radial structure tensor analysis.

J. Med. Imaging 4:044502 (2017)

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Open Access Green as soon as Postprint is submitted to ZB.

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This paper presents a local intensity structure analysis based on an intensity targeted radial structure tensor (ITRST) and the blob-like structure enhancement filter based on it (ITRST filter) for the mediastinal lymph node detection algorithm from chest computed tomography (CT) volumes. Although the filter based on radial structure tensor analysis (RST filter) based on conventional RST analysis can be utilized to detect lymph nodes, some lymph nodes adjacent to regions with extremely high or low intensities cannot be detected. Therefore, we propose the ITRST filter, which integrates the prior knowledge on detection target intensity range into the RST filter. Our lymph node detection algorithm consists of two steps: (1) obtaining candidate regions using the ITRST filter and (2) removing false positives (FPs) using the support vector machine classifier. We evaluated lymph node detection performance of the ITRST filter on 47 contrast-enhanced chest CT volumes and compared it with the RST and Hessian filters. The detection rate of the ITRST filter was 84.2% with 9.1 FPs/volume for lymph nodes whose short axis was at least 10 mm, which outperformed the RST and Hessian filters.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Computer-aided Detection ; Local Intensity Structure Analysis ; Lung Cancer ; Structure Tensor

ISSN (print) / ISBN 2329-4302

e-ISSN 2329-4310

Journal Journal of medical imaging

Quellenangaben Volume: 4, Issue: 4, Article Number: 044502

Publisher SPIE

Publishing Place Bellingham, Wash.

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute for Machine Learning in Biomed Imaging (IML)