Soudeh Saien1, Hamid Abrishami Moghaddam2, Mohsen Fathian3. 1. Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran. soudeh_saien@yahoo.com. 2. Department of Electrical Engineering, K.N.Toosi University of Technology, Tehran, Iran. 3. Department of Computer Engineering and Information Technology, Hamedan University of Technology, Hamedan, Iran.
Abstract
PURPOSE: This work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes. METHODS: The 3D region of each detected nodule candidate is first reconstructed using the sparse field method for accurately segmenting the objects. This technique enhances the level set modeling by restricting the computations to a narrow band near the evolving curve. Then, a set of 2D and 3D relevant features are extracted for each segmented candidate. Subsequently, a hybrid undersampling/boosting algorithm called RUSBoost is applied to analyze the features and discriminate real nodules from non-nodules. RESULTS: The performance of the proposed scheme was evaluated by using 70 CT images, randomly selected from the Lung Image Database Consortium and containing 198 nodules. Applying RUSBoost classifier exhibited a better performance than some commonly used classifiers. It effectively reduced the average number of FPs to only 3.9 per scan based on a fivefold cross-validation. CONCLUSION: The practical implementation, applicability for different nodule types and adaptability in handling the imbalanced data classification insure the improvement in lung nodules detection by utilizing this new approach.
PURPOSE: This work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes. METHODS: The 3D region of each detected nodule candidate is first reconstructed using the sparse field method for accurately segmenting the objects. This technique enhances the level set modeling by restricting the computations to a narrow band near the evolving curve. Then, a set of 2D and 3D relevant features are extracted for each segmented candidate. Subsequently, a hybrid undersampling/boosting algorithm called RUSBoost is applied to analyze the features and discriminate real nodules from non-nodules. RESULTS: The performance of the proposed scheme was evaluated by using 70 CT images, randomly selected from the Lung Image Database Consortium and containing 198 nodules. Applying RUSBoost classifier exhibited a better performance than some commonly used classifiers. It effectively reduced the average number of FPs to only 3.9 per scan based on a fivefold cross-validation. CONCLUSION: The practical implementation, applicability for different nodule types and adaptability in handling the imbalanced data classification insure the improvement in lung nodules detection by utilizing this new approach.
Keywords:
False positives reduction; Imbalanced data classification; Lung nodules detection; RUSBoost classifier; Sparse field method
Authors: Zhen Ma; João Manuel R S Tavares; Renato Natal Jorge; T Mascarenhas Journal: Comput Methods Biomech Biomed Engin Date: 2010 Impact factor: 1.763
Authors: Jorge Juan Suárez-Cuenca; Pablo G Tahoces; Miguel Souto; María J Lado; Martine Remy-Jardin; Jacques Remy; Juan José Vidal Journal: Comput Biol Med Date: 2009-08-05 Impact factor: 4.589
Authors: J H Austin; N L Müller; P J Friedman; D M Hansell; D P Naidich; M Remy-Jardin; W R Webb; E A Zerhouni Journal: Radiology Date: 1996-08 Impact factor: 11.105
Authors: Colin Jacobs; Eva M van Rikxoort; Thorsten Twellmann; Ernst Th Scholten; Pim A de Jong; Jan-Martin Kuhnigk; Matthijs Oudkerk; Harry J de Koning; Mathias Prokop; Cornelia Schaefer-Prokop; Bram van Ginneken Journal: Med Image Anal Date: 2013-12-17 Impact factor: 8.545
Authors: Thomas Weikert; Tugba Akinci D'Antonoli; Jens Bremerich; Bram Stieltjes; Gregor Sommer; Alexander W Sauter Journal: Contrast Media Mol Imaging Date: 2019-07-01 Impact factor: 3.161