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

• Published in: Medical & biological engineering & computing Vol. 55; no. 8; pp. 1199 - 1213
• Main Authors: Filho, Antonio Oseas de Carvalho, Silva, Aristófanes Corrêa, de Paiva, Anselmo Cardoso, Nunes, Rodolfo Acatauassú, Gattass, Marcelo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cancer; Classification; Computer Applications; Consortia; False Positive Reactions; Female; Human Physiology; Humans; Image classification; Image databases; Image detection; Image processing; Imaging; Imaging, Three-Dimensional - methods; Lung cancer; Lung nodules; Machine Learning; Male; Methodology; Middle Aged; Nodules; Object recognition; Original Article; Paper machines; Pattern recognition; Pattern Recognition, Automated - methods; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Reproducibility of Results; Sensitivity and Specificity; Solitary Pulmonary Nodule - diagnostic imaging; Solitary Pulmonary Nodule - pathology; Test procedures; Tomography, X-Ray Computed - methods; Medical image; Cylinder-based analysis; Shape diagrams; Lung cancer
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-016-1582-x

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 k = 5 , 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.