Analysis of Computational Techniques Used in Pulmonary Nodule Segmentation
Abstract
According to WHO (World Health Organization) lung cancer is the most common cause of death by cancer and your early detection is directly related to the effectiveness of the treatment and with the chances of cure. One of the most used techniques to diagnose lung cancer involves digital image analysis obtained by conventional x-ray and computed tomography. Also image processing has been standing out as one of the research fields in ascension. This work aims the comparative analysis of different digital image processing techniques to identify and validate which of them are most applicable to the use for lung nodules segmentation on computed tomography scans. As its final objective, this work aims to describe and integrate the validated techniques to an open platform to support lung cancer diagnosis.
References
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Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al. (2011) "The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans". Medical Physics, 38: 915-931.
Ferreira-Junior, J.R., Oliveira, M.C. & Azevedo-Marques, P.M. (2016) "Cloud-based NoSQL open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research". Journal of Digital Imaging, 29:716-729.
