Abstract
Purpose
This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning.
Methods
Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized to provide feature descriptors of malignant and benign nodules and non-nodule regions of interest. Support vector machines (SVMs) and k-nearest neighbor (kNN) classifiers in serial and two-tier cascade frameworks are optimized and analyzed for optimal classification results of nodules.
Results
A total of 1191 nodule and non-nodule samples from the Lung Image Data Consortium database is used for analysis. Classification using SVM and kNN classifiers is examined. The classification results from the two-tier cascade SVM using Gabor features showed overall better results for identifying non-nodules, malignant and benign nodules with average area under the receiver operating characteristics (AUC-ROC) curves of 0.99 and average f1-score of 0.975 over the two tiers.
Conclusion
In the results, higher overall AUCs and f1-scores were obtained for the non-nodules cases using any of the three features, showing the greatest distinguishability over nodules (benign/malignant). SVM and kNN classifiers were used for benign, malignant and non-nodule classification, where Gabor proved to be the most effective of the features for classification. The cascaded framework showed the greatest distinguishability between benign and malignant nodules.
Similar content being viewed by others
References
Centers for Disease Control and Prevention. National Center for Health Statistics (2016) CDC WONDER on-line database, compiled from compressed mortality file 1999–2014 Series 20 No. 2T
American Cancer Society (2017) Non-Small Cell Lung Cancer Stages. www.cancer.org
Zaho B, Gamsu G, Ginsberg MS, Jiang L, Schwartz LH (2003) Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J Appl Clin Med Phys 4:248–260
Ost DE, Gould MK (2012) Decision making in patients with pulmonary nodules. Am J Respir Crit Care Med 185(4):363–372
Dilger SK, Judisch A, Uthoff J, Hammond E, Newell JD, Sieren JC (2015) Improved pulmonary nodule classification utilizing lung parenchyma texture features. J Med Imaging 2(4):041004
Farag A (2012) Modeling small objects under uncertainties: novel algorithms and applications. PhD Dissertation, University of Louisville, Department of Electrical and Computer Engineering
Prokop M, Sluimer I, Schilham A, van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 25(4):385–405
Fujita H, Itoh S, Lee Y, Hara T, Ishigaki T (2001) Automated detection of pulmonary nodules in helical ct images based on an improved template-matching technique. IEEE Trans Med Imaging 20:595–604
Yankelevitz DF, Kostis WJ, Reeves AP, Henschke CI (2003) Three dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical ct images. IEEE Trans Med Imaging 22:1259–1274
Kostis WJ, Yankelevitz DF, Reeves AP, Henschke CI (2004) Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up. Radiology 231:446–52
Farag A, Elhabian S, Graham J, Farag AA, Falk R (2010) Toward precise pulmonary nodule descriptors for nodule type classification. In: Proceedings of the 13th international conference on medical image computing and computer assisted intervention (MICCAI), pp 626–633
Lee S, Kouzani A, Hu E (2010) Automated detection of lung nodules in computed tomography images: a review. Mach Vis Appl 23:151–163
Cootes T, Edwards G, Taylor C (1998) Active appearance models. In: Proceedings of the European conference on computer vision, ECCV’98, pp 484–498
Orozco HM, Villegas O, Sanchez VGC, Domınguez HdJO, Alfaro MdJN (2015) Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed Eng Online 14(1):9
Reeves AP, Xie Y, Jirapatnakul A (2015) Automated pulmonary nodule CT image characterization in lung cancer screening. Int J Comput Assist Radiol Surg 11(1):73–88
Felix A, Oliveira M, Machado A, Raniery J (2016) Using 3D texture and margin sharpness features on classification of small pulmonary nodules. In: 29th conference on graphics, patterns and images (SIBGRAPI)
van Ginneken B, Katsuragwa S, Romney B, Doi KM, Viergever MA (2002) Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Trans Med Imaging 21(2):139–149.
Firmino M, Angelo G, Morais H, Dantas MR, Valentim R (2016) Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. J Negat Results Biomed. doi:10.1186/s12938-015-0120-7
Farag A, Graham J, Farag AA, Elshazly S, Falk R (2010) Parametric and non-parametric nodule models: design and evaluation. In: Proceedings of third international workshop on pulmonary image processing in conjunction with MICCAI’10, Beijing, Sept 2010
Armato G, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP (2004) Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232(3):739–748
LIDC-IDRI—The Cancer Imaging Archive (TCIA) Public Access—Cancer Imaging Archive Wiki. https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. In: IEEE transactions on pattern analysis and machine intelligence, vol 24, pp 971–987
Liu C (2004) Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans Pattern Anal Mach Intell 26(5):572–581
Arya S, Mount DM, Netanyahu NS, Silverman R, Wu AY (1998) An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J ACM 45(6):891–923
Vapnik V (1982) Estimation of dependences based on empirical data. Springer, New York
Kumar D, Wong A, Clausi DA (2015) Lung nodule classification using deep features in CT images. In: 2015 12th conference on computer and robot vision (CRV), pp 133–138
Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther 8:2015–2022
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare they have no conflict of interests.
Ethical approval
All procedures performed in studies involving human participants were in accordance with ethical standards of the institutional and/or national research committee.
Funding
This research was conducted in collaboration between Kentucky Imaging Technologies and the University of Louisville, Computer Vision and Imaging Processing Laboratory.
Informed consent
The research conducted in this paper utilized the retrospective and publicly available LIDC database, and as such no formal consent was necessary.
Rights and permissions
About this article
Cite this article
Farag, A.A., Ali, A., Elshazly, S. et al. Feature fusion for lung nodule classification. Int J CARS 12, 1809–1818 (2017). https://doi.org/10.1007/s11548-017-1626-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-017-1626-1