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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 10/2017

09.05.2017 | Original Article

Automatic classification of lung nodules on MDCT images with the temporal subtraction technique

verfasst von: Yuriko Yoshino, Takahiro Miyajima, Huimin Lu, Jookooi Tan, Hyoungseop Kim, Seiichi Murakami, Takatoshi Aoki, Rie Tachibana, Yasushi Hirano, Shoji Kido

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 10/2017

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Abstract

Purpose

A temporal subtraction (TS) image is obtained by subtracting a previous image, which is warped to match the structures of the previous image and the related current image. The TS technique removes normal structures and enhances interval changes such as new lesions and substitutes in existing abnormalities from a medical image. However, many artifacts remaining on the TS image can be detected as false positives.

Method

This paper presents a novel automatic segmentation of lung nodules using the Watershed method, multiscale gradient vector flow snakes and a detection method using the extracted features and classifiers for small lung nodules (20 mm or less).

Result

Using the proposed method, we conduct an experiment on 30 thoracic multiple-detector computed tomography cases including 31 small lung nodules.

Conclusion

The experimental results indicate the efficiency of our segmentation method.
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Metadaten
Titel
Automatic classification of lung nodules on MDCT images with the temporal subtraction technique
verfasst von
Yuriko Yoshino
Takahiro Miyajima
Huimin Lu
Jookooi Tan
Hyoungseop Kim
Seiichi Murakami
Takatoshi Aoki
Rie Tachibana
Yasushi Hirano
Shoji Kido
Publikationsdatum
09.05.2017
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 10/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-017-1598-1

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