Erschienen in:
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.