3D shape analysis to reduce false positives for lung nodule detection systems.

Abstract:

:Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC-IDRI database, and cross-validation with k-fold, where [Formula: see text], was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.

journal_name

Med Biol Eng Comput

authors

Filho AOC,Silva AC,de Paiva AC,Nunes RA,Gattass M

doi

10.1007/s11517-016-1582-x

subject

Has Abstract

pub_date

2017-08-01 00:00:00

pages

1199-1213

issue

8

eissn

0140-0118

issn

1741-0444

pii

10.1007/s11517-016-1582-x

journal_volume

55

pub_type

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