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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

16.06.2017 | Original Article

Feature fusion for lung nodule classification

verfasst von: Amal A. Farag, Asem Ali, Salwa Elshazly, Aly A. Farag

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 10/2017

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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.

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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 4:248–260CrossRef

Ost DE, Gould MK (2012) Decision making in patients with pulmonary nodules. Am J Respir Crit Care Med 185(4):363–372CrossRefPubMedPubMedCentral

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):041004CrossRef

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):385–405CrossRefPubMed

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 20:595–604CrossRefPubMed

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 22:1259–1274CrossRefPubMed

10.

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 231:446–52CrossRefPubMed

11.

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

12.

Lee S, Kouzani A, Hu E (2010) Automated detection of lung nodules in computed tomography images: a review. Mach Vis Appl 23:151–163CrossRef

13.

Cootes T, Edwards G, Taylor C (1998) Active appearance models. In: Proceedings of the European conference on computer vision, ECCV’98, pp 484–498

14.

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):9CrossRef

15.

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):73–88CrossRefPubMed

16.

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)

17.

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.

18.

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

19.

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

20.

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):739–748CrossRefPubMed

21.

LIDC-IDRI—The Cancer Imaging Archive (TCIA) Public Access—Cancer Imaging Archive Wiki. https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI

22.

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

23.

Liu C (2004) Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans Pattern Anal Mach Intell 26(5):572–581CrossRefPubMed

24.

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):891–923CrossRef

25.

Vapnik V (1982) Estimation of dependences based on empirical data. Springer, New York

26.

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

27.

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

Titel: Feature fusion for lung nodule classification
verfasst von: Amal A. Farag
Asem Ali
Salwa Elshazly
Aly A. Farag
Publikationsdatum: 16.06.2017
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 10/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1626-1

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Abstract

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