Abstract
Early detection of pulmonary lung nodules plays a significant role in the diagnosis of lung cancer. Computed tomography (CT) and chest radiographs (CRs) are currently being used by radiologists to detect such nodules. In this paper, we present a novel cluster-based classifier architecture for lung nodule computer-aided detection systems in both modalities. We propose a novel optimized method of feature selection for both cluster and classifier components. For CRs, we make use of an independent database comprising of 160 cases with a total of 173 nodules for training purposes. Testing is implemented on a publicly available database created by the Standard Digital Image Database Project Team of the Scientific Committee of the Japanese Society of Radiological Technology (JRST). The JRST database comprises 154 CRs containing one radiologist-confirmed nodule in each. In this research, we exclude 14 cases from the JRST database that contain lung nodules in the retrocardiac and subdiaphragmatic regions of the lung. For CT scans, the analysis is based on threefold cross-validation performance on 107 cases from publicly available dataset created by Lung Image Database Consortium comprised of 280 nodules. Overall, with a specificity of 3 false positives per case/patient on average, we show a classifier performance boost of 7.7% for CRs and 5.0% for CT scans when compared to a single aggregate classifier architecture.
Similar content being viewed by others
References
The American Cancer Society (2015) Cancer facts and figures. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2015.html. Accessed 30 Sept 2017
Stewart BW, Wild CP (2017) World cancer report 2014, vol 505. International Agency for Research on Cancer, World Health Organization
Cancer Research UK, Lung cancer survival statistics. http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/lung-cancer/survival. Accessed 7 Aug 2017
Henschke CI, McCauley DI, Yankelevitz DF, Naidich DP, McGuniness G, Miettinen OS, Libby DM, Pasmantier MW, Koizumi J, Altorki NK, Smith JP (1999) Early cancer action project: overall design and findings from baseline screening. Lancet 354(1973):99–105
Hardie RC, Rogers SK, Wilson T, Rogers A (2008) Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs. Med Image Anal 12(3):240–258
Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3):390–406
Okumura T, Miwa T, Kako J, Yamamoto S, Matsumoto M, Tateno Y, Linuma T, Matshmoto T (1998) Variable N-Quoit filter applied for automatic detection of lung cancer by X-ray CT. In: Computer assisted radiology and surgery (CAR 1998), pp 242–247
Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, Eguchi K (1998) Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput Med Imaging Graph 22(2):157–167
Hadjiiski L, Sahiner B, Chan HP, Petrick N, Helvie M (1999) Classification of malignant and benign masses based on hybrid ART2LDA approach. IEEE Trans Med Imaging 18(12):1178–1187
Armato SG III, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med Phys 28(8):1552–1561
Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging 20(7):595–604
Näppi J, Yoshida H (2002) Automated detection of polyps with CT colonography: evaluation of volumetric features for reduction of false-positive findings. Acad Radiol 9(4):386–397
Rubin GD, Lyo JK, Paik DS, Sherbondy AJ, Chow LC, Leung AN, Napel S (2005) Pulmonary nodules on multi–detector row ct scans: performance comparison of radiologists and computer-aided detection. Radiology 234(1):274–283
Wei L, Yang Y, Nishikawa RM, Jiang Y (2005) A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans Med Imaging 24(3):371–380
Shiraishi J, Li Q, Suzuki K, Engelmann R, Doi K (2006) Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. Med Phys 33(7):2642–2653
Gori I, Fantacci ME, Martinez AP, Retico A (2007) An automated system for lung nodule detection in low-dose computed tomography. In: Giger ML, Karssemeiger N (eds) Proceedings of the SPIE on medical imaging 2007: computer-aided diagnosis, San Diego, CA, United States, vol 6514, p 65143R
Narayanan BN, Hardie RC, Kebede TM (2016) Analysis of various classification techniques for computer aided detection system of pulmonary nodules in CT. In: Aerospace and electronics conference (NAECON) and Ohio innovation summit (OIS), pp 88–93
Gruetzemacher R, Gupta A (2016) Using deep learning for pulmonary nodule detection and diagnosis. In: Twenty-second American conference on information systems, San Diego
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
Yang H, Yu H, Wang G (2016) Deep Learning for the Classification of Lung Nodules. arXiv preprint arXiv:1611.06651
Setio AA, Jacobs C, Ciompi F, van Riel SJ, Wille MMW, Dirksen A et al (2015) Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model. In: Hadjiiski LM, Tourassi GD (eds) Proceedings of the SPIE medical imaging 2015: computer-aided diagnosis, Orlando, FL, United States, vol 9414, p 94141O
Sun W, Zheng B, Qian W (2016) Computer aided lung cancer diagnosis with deep learning algorithms. In: Tourassi GD, Armato SG (eds) Proceedings of the SPIE medical imaging 2016: computer-aided diagnosis, San Diego, CA, United States, vol 9785, p 97850Z
Shaukat F, Raja G, Gooya A, Frangi AF (2017) Fully automatic and accurate detection of lung nodules in CT images using a hybrid feature set. Med Phys 44:3615–3629
Jaffar MA, Siddiqui AB, Mushtaq M (2017) Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution. Cluster Comput. doi:10.1007/s10586-017-0876-6
Liu JK, Jiang HY, He CG, Wang Y, Wang P, Ma H (2017) An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images. J Med Syst 41(2):30
Javaid M, Javid M, Rehman MZU, Shah SIA (2016) A novel approach to CAD system for the detection of lung nodules in CT images. Comput Methods Progr Biomed 135:125–139
Van Ginneken B, Armato SG, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME, Camarlinghi N (2010) Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal 14(6):707–722
Armato SG III, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19(5):1303–1311
Armato SG III, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Reeves AP (2004) Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232(3):739–748
Wiemker R, Rogalla P, Opfer R, Ekin A, Romano V, Bülow T (2006) Comparative performance analysis for computer aided lung nodule detection and segmentation on ultra-low-dose vs. standard-dose CT. In: Jiang Y, Eckstein MP (eds) Proceedings of the SPIE medical imaging 2006: image perception, observer performance, and technology assessment, San Diego, CA, United States, vol 6146, p 614605
Das M, Mühlenbruch G, Mahnken AH, Flohr TG, Gündel L, Stanzel S, Wildberger JE (2006) Small Pulmonary nodules: effect of two computer-aided detection systems on radiologist performance 1. Radiology 241(2):564–571
Yuan R, Vos PM, Cooperberg PL (2006) Computer-aided detection in screening CT for pulmonary nodules. Am J Roentgenol 186(5):1280–1287
Gurung J, Maataoui A, Khan M, Wetter A, Harth M, Jacobi V, Vogl TJ (2006) Automated detection of lung nodules in multidetector CT: influence of different reconstruction protocols on performance of a software prototype. RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 178(1):71–77
Opfer R, Wiemker R (2007) Performance analysis for computer-aided lung nodule detection on LIDC data. In: Jiang Y, Sahiner B (eds) Proceedings of the SPIE medical imaging 2007: image perception, observer performance, and technology assessment, San Diego, CA, United States, vol 6515, p 65151C
Sahiner B, Hadjiiski LM, Chan HP, Shi J, Cascade PN, Kazerooni EA, Song T (2007) Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: observer performance study. In: Jiang Y, Sahiner B (eds) Proceedings of the SPIE medical imaging 2007: image perception, observer performance, and technology assessment, San Diego, CA, United States, vol 6515, p 65151D
Buhmann S, Herzog P, Liang J, Wolf M, Salganicoff M, Kirchhoff C, Becker CH (2007) Clinical evaluation of a computer-aided diagnosis (CAD) prototype for the detection of pulmonary embolism. Acad Radiol 14(6):651–658
Schilham AM, Van Ginneken B, Loog M (2006) A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Med Image Anal 10(2):247–258
Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678
Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu KI, Doi K (2000) Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am J Roentgenol 174(1):71–74
Kobatake H, Hashimoto S (1999) Convergence index filter for vector fields. IEEE Trans Image Process 8(8):1029–1038
Wei J, Hagihara Y, Kobatake H (1999) Detection of rounded opacities on chest radiographs using convergence index filter. In: Proceedings of IEEE international conference on image analysis and processing, pp 757–761
Liu H, Motoda H (2012) Feature selection for knowledge discovery and data mining, vol 454. Springer, Berlin
Druzhkov PN, Kustikova VD (2016) A survey of deep learning methods and software tools for image classification and object detection. Pattern Recogn Image Anal 26(1):9
Pintea SL, Mettes PS, van Gemert JC, Smeulders AW (2016) Featureless: Bypassing feature extraction in action categorization. In: IEEE international conference on image processing (ICIP), pp 196–200
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Rights and permissions
About this article
Cite this article
Narayanan, B.N., Hardie, R.C., Kebede, T.M. et al. Optimized feature selection-based clustering approach for computer-aided detection of lung nodules in different modalities. Pattern Anal Applic 22, 559–571 (2019). https://doi.org/10.1007/s10044-017-0653-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10044-017-0653-4