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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

09.08.2017 | Original Article

A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection

verfasst von: Soudeh Saien, Hamid Abrishami Moghaddam, Mohsen Fathian

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2018

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Abstract

Purpose

This work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes.

Methods

The 3D region of each detected nodule candidate is first reconstructed using the sparse field method for accurately segmenting the objects. This technique enhances the level set modeling by restricting the computations to a narrow band near the evolving curve. Then, a set of 2D and 3D relevant features are extracted for each segmented candidate. Subsequently, a hybrid undersampling/boosting algorithm called RUSBoost is applied to analyze the features and discriminate real nodules from non-nodules.

Results

The performance of the proposed scheme was evaluated by using 70 CT images, randomly selected from the Lung Image Database Consortium and containing 198 nodules. Applying RUSBoost classifier exhibited a better performance than some commonly used classifiers. It effectively reduced the average number of FPs to only 3.9 per scan based on a fivefold cross-validation.

Conclusion

The practical implementation, applicability for different nodule types and adaptability in handling the imbalanced data classification insure the improvement in lung nodules detection by utilizing this new approach.

Vorheriger Artikel Patient-specific estimation of detailed cochlear shape from clinical CT images

Nächster Artikel A novel integration of spectral-domain optical-coherence-tomography and laser-ablation system for precision treatment

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Titel: A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection
verfasst von: Soudeh Saien
Hamid Abrishami Moghaddam
Mohsen Fathian
Publikationsdatum: 09.08.2017
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2018
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1656-8

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Abstract

Purpose

Methods

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Conclusion

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