A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection

PurposeThis work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes.MethodsThe 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.ResultsThe 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.ConclusionThe 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.

[1]  Wei Li,et al.  A multi-kernel based framework for heterogeneous feature selection and over-sampling for computer-aided detection of pulmonary nodules , 2017, Pattern Recognit..

[2]  Zhi-Hua Zhou,et al.  Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Marcelo Gattass,et al.  Automatic segmentation of lung nodules with growing neural gas and support vector machine , 2012, Comput. Biol. Medicine.

[4]  João Manuel R. S. Tavares,et al.  Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images , 2017, Medical Image Anal..

[5]  Anselmo Cardoso de Paiva,et al.  Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM , 2014, Eng. Appl. Artif. Intell..

[6]  Jan Cornelis,et al.  A novel computer-aided lung nodule detection system for CT images. , 2011, Medical physics.

[7]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[8]  Zohreh Azimifar,et al.  Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system , 2013, Comput. Biol. Medicine.

[9]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.

[10]  Temesguen Messay,et al.  A new computationally efficient CAD system for pulmonary nodule detection in CT imagery , 2010, Medical Image Anal..

[11]  Zhengrong Liang,et al.  Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Hierarchical Vector Quantization Scheme , 2015, IEEE Journal of Biomedical and Health Informatics.

[12]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[13]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[15]  Aaron Fenster,et al.  Lung tumours segmentation on CT using sparse field active model , 2011, Medical Imaging.

[16]  Ilaria Gori,et al.  Combination of computer-aided detection algorithms for automatic lung nodule identification , 2011, International Journal of Computer Assisted Radiology and Surgery.

[17]  Syed Irtiza Ali Shah,et al.  A novel approach to CAD system for the detection of lung nodules in CT images , 2016, Comput. Methods Programs Biomed..

[18]  Piergiorgio Cerello,et al.  A novel multithreshold method for nodule detection in lung CT. , 2009, Medical physics.

[19]  Tae-Sun Choi,et al.  Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor , 2014, Comput. Methods Programs Biomed..

[20]  Jorge Juan Suárez-Cuenca,et al.  Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images , 2009, Comput. Biol. Medicine.

[21]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, ICDM.

[22]  Zhen Ma,et al.  A review of algorithms for medical image segmentation and their applications to the female pelvic cavity , 2010, Computer methods in biomechanics and biomedical engineering.

[23]  Hiroshi Fujita,et al.  Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter , 2012, International Journal of Computer Assisted Radiology and Surgery.

[24]  Ross T. Whitaker,et al.  A Level-Set Approach to 3D Reconstruction from Range Data , 1998, International Journal of Computer Vision.

[25]  Bram van Ginneken,et al.  Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images , 2014, Medical Image Anal..

[26]  Jamshid Dehmeshki,et al.  Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images , 2009, IEEE Transactions on Biomedical Engineering.

[27]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[28]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[29]  Zaid J. Towfic,et al.  The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation , 2007, SPIE Medical Imaging.

[30]  João Paulo Papa,et al.  Efficient supervised optimum-path forest classification for large datasets , 2012, Pattern Recognit..

[31]  Wei Li,et al.  nsemble-based hybrid probabilistic sampling for imbalanced data earning in lung nodule CAD , 2014 .

[32]  J. Austin,et al.  Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society. , 1996, Radiology.

[33]  Giorgio Valentini,et al.  Support vector machines for candidate nodules classification , 2005, Neurocomputing.

[34]  S. Armato,et al.  Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. , 2003, Medical physics.

[35]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[36]  Hengyong Yu,et al.  Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition , 2016, Comput. Methods Programs Biomed..

[37]  Bram van Ginneken,et al.  A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification , 2009, Medical Image Anal..

[38]  David G. Stork,et al.  Pattern Classification , 1973 .

[39]  Ulas Bagci,et al.  Computer-assisted detection of infectious lung diseases: A review , 2012, Comput. Medical Imaging Graph..

[40]  M. Masotti,et al.  Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. , 2011, Medical physics.

[41]  Hamid Abrishami Moghaddam,et al.  Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels , 2014, Comput. Biol. Medicine.

[42]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[43]  David Gur,et al.  An automated CT based lung nodule detection scheme using geometric analysis of signed distance field. , 2008, Medical physics.

[44]  Xuelong Li,et al.  An Efficient MRF Embedded Level Set Method for Image Segmentation , 2015, IEEE Transactions on Image Processing.

[45]  K. Doi,et al.  Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. , 2008, Academic Radiology.

[46]  Abbas Z. Kouzani,et al.  Automated detection of lung nodules in computed tomography images: a review , 2010, Machine Vision and Applications.