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

作者：de Carvalho Filho Antonio Oseas^*; Silva Aristofanes Correa; de Paiva Anselmo Cardoso; Nunes Rodolfo Acatauassu; Gattass Marcelo

来源：Medical & Biological Engineering & Computing, 2017, 55(8): 1199-1213.

DOI：10.1007/s11517-016-1582-x

• Lung cancer
• Medical image
• Shape diagrams
• Cylinder-based analysis

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 , 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.

• 出版日期2017-8