Pablo G Cavalcanti1, Shahram Shirani1, Jacob Scharcanski1, Crystal Fong1, Jane Meng1, Jane Castelli1, David Koff1. 1. 1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Abstract
BACKGROUND: Lung cancer results in the highest number of cancer deaths worldwide. The segmentation of lung nodules is an important task in computer systems to help physicians differentiate malignant lesions from benign lesions. However, it has already been observed that this may be a difficult task, especially when nodules are connected to an anatomical structure. METHODS: This paper proposes a method to estimate the background of the nodule area and how this estimation is used to facilitate the segmentation task. RESULTS: Our experiments indicate more than 99% of accuracy with less than 1% of false positive rate (FPR). CONCLUSIONS: The proposed methods achieved better results than a state-of-the-art approach, indicating potential to be used in medical image processing systems.
BACKGROUND: Lung cancer results in the highest number of cancer deaths worldwide. The segmentation of lung nodules is an important task in computer systems to help physicians differentiate malignant lesions from benign lesions. However, it has already been observed that this may be a difficult task, especially when nodules are connected to an anatomical structure. METHODS: This paper proposes a method to estimate the background of the nodule area and how this estimation is used to facilitate the segmentation task. RESULTS: Our experiments indicate more than 99% of accuracy with less than 1% of false positive rate (FPR). CONCLUSIONS: The proposed methods achieved better results than a state-of-the-art approach, indicating potential to be used in medical image processing systems.
Authors: Samuel G Armato; Geoffrey McLennan; Luc Bidaut; Michael F McNitt-Gray; Charles R Meyer; Anthony P Reeves; Binsheng Zhao; Denise R Aberle; Claudia I Henschke; Eric A Hoffman; Ella A Kazerooni; Heber MacMahon; Edwin J R Van Beeke; David Yankelevitz; Alberto M Biancardi; Peyton H Bland; Matthew S Brown; Roger M Engelmann; Gary E Laderach; Daniel Max; Richard C Pais; David P Y Qing; Rachael Y Roberts; Amanda R Smith; Adam Starkey; Poonam Batrah; Philip Caligiuri; Ali Farooqi; Gregory W Gladish; C Matilda Jude; Reginald F Munden; Iva Petkovska; Leslie E Quint; Lawrence H Schwartz; Baskaran Sundaram; Lori E Dodd; Charles Fenimore; David Gur; Nicholas Petrick; John Freymann; Justin Kirby; Brian Hughes; Alessi Vande Casteele; Sangeeta Gupte; Maha Sallamm; Michael D Heath; Michael H Kuhn; Ekta Dharaiya; Richard Burns; David S Fryd; Marcos Salganicoff; Vikram Anand; Uri Shreter; Stephen Vastagh; Barbara Y Croft Journal: Med Phys Date: 2011-02 Impact factor: 4.071