Abstract
The cancer of lung has been one of the major threats to human life for decades in developed and developing countries. The Computer Aided Detection CAD could be a powerful tool for initial lung nodule detection and preventing the deaths caused by the lung tumor. In this paper, an advanced technique for lung-nodule detection by using a hybrid feature set and artificial neural network is proposed. Initially, the lung volume is segmented from the input Computed Tomography image using optimal thresholding which is followed by image enhancement using with multi scale dot augmentation filtering. Next, lung nodule candidates have been detected from enhanced image and certain features are extracted. The set feature consists of the texture features, shape 2D and 3D and intensity. Finally, lung nodule’s classification is attained using two-layer feed forward neural network. The Lung Image Database Consortium dataset has been used to evaluate the novel system which achieved a sensitivity of 95.5% with only 5.72 FP per scan.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akram S, Javed MY, Akram MU, Qamar U, Hassan A (2016) Pulmonary nodules detection and classification using hybrid features from computerized tomographic images. J Med Imaging Health Inform 6(1):252–259. https://doi.org/10.1166/jmihi.2016.1600
Ali AM, Farag AA (2008) Automatic lung segmentation of volumetric low-dose CT scans using graph cuts. Adv Vis Comput. https://doi.org/10.1007/978-3-540-89639-5_25
Armato SG, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H (1999) Computerized detection of pulmonary nodules on CT scans. RadioGraphics 19(5):1303–1311. https://doi.org/10.1148/radiographics.19.5.g99se181303
Assefa M, Faye I, Malik AS, Shoaib M (2013) Lung nodule detection using multi-resolution analysis. 2013 ICME International Conference on Complex Medical Engineering. https://doi.org/10.1109/iccme.2013.6548290
Bergtholdt M, Wiemker R, Klinder T (2016) Pulmonary nodule detection using a cascaded SVM classifier. Med Imaging 2016 Comput Aided Diagn. https://doi.org/10.1117/12.2216747
Choi W, Choi T (2012) Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images. Inf Sci 212:57–78. https://doi.org/10.1016/j.ins.2012.05.008
Choi W, Choi T (2013) Automated pulmonary nodule detection system in computed tomography images: a hierarchical block classification approach. Entropy 15(2):507–523. https://doi.org/10.3390/e15020507
Choi W, Choi T (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Programs Biomed 113(1):37–54. https://doi.org/10.1016/j.cmpb.2013.08.015
Da Silva Sousa JR, Silva AC, De Paiva AC, Nunes RA (2010) Methodology for automatic detection of lung nodules in computerized tomography images. Comput Methods Programs Biomed 98(1):1–14. https://doi.org/10.1016/j.cmpb.2009.07.006
De Nunzio G, Tommasi E, Agrusti A, Cataldo R, De Mitri I, Favetta M, Oliva P (2009) Automatic lung segmentation in CT images with accurate handling of the Hilar Region. J Digit Imaging 24(1):11–27. https://doi.org/10.1007/s10278-009-9229-1
Dehmeshki J, Ye X, Lin X, Valdivieso M, Amin H (2007) Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput Med Imaging Graph 31(6):408–417. https://doi.org/10.1016/j.compmedimag.2007.03.002
Diaz JM, Pinon RC, Solano G (2014) Lung cancer classification using genetic algorithm to optimize prediction models. IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications. https://doi.org/10.1109/iisa.2014.6878770
Ding J, Li A, Hu Z, Wang L (2017) Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-319-66179-7_64
Dou Q, Chen H, Yu L, Qin J, Heng P (2017) Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 64(7):1558–1567. https://doi.org/10.1109/tbme.2016.2613502
Espejo P, Ventura S, Herrera F (2010) A Survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern Part C (Appl Rev) 40(2):121–144. https://doi.org/10.1109/tsmcc.2009.2033566
Guo W, Wei Y, Hanxun Z, DingYe X (2009). An adaptive lung nodule detection algorithm. 2009 Chinese Control and Decision Conference. https://doi.org/10.1109/ccdc.2009.5192686
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621. https://doi.org/10.1109/tsmc.1973.4309314
He X, Yu S, Xu P, Wang J, Zhan M (2012) Combining red and blue-detuned optical potentials to form a Lamb-Dicke trap for a single neutral atom. Opt Express 20(4):3711. https://doi.org/10.1364/oe.20.003711
Henschke CI (2001) Early lung cancer action project: a summary of the findings on baseline screening. Oncologist 6(2):147–152. https://doi.org/10.1634/theoncologist.6-2-147
Hu S, Hoffman E, Reinhardt J (2001) Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging 20(6):490–498. https://doi.org/10.1109/42.929615
Jiantao P, Paik DS, Xin M, Roos J, Rubin GD (2011) Shape “break-and-repair” strategy and its application to automated medical image segmentation. IEEE Trans Visual Comput Graphics 17(1):115–124. https://doi.org/10.1109/tvcg.2010.56
Kostis W, Reeves A, Yankelevitz D, Henschke C (2003) Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical ct images. IEEE Trans Med Imaging 22(10):1259–1274. https://doi.org/10.1109/tmi.2003.817785
Kubota T, Jerebko AK, Dewan M, Salganicoff M, Krishnan A (2011) Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med Image Anal 15(1):133–154. https://doi.org/10.1016/j.media.2010.08.005
Lee S, Kouzani A, Hu E (2010) Random forest based lung nodule classification aided by clustering. Comput Med Imaging Graph 34(7):535–542. https://doi.org/10.1016/j.compmedimag.2010.03.006
Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys 30(8):2040–2051. https://doi.org/10.1118/1.1581411
Liu Y, Yang J, Zhao D, Liu J (2009) Computer aided detection of lung nodules based on voxel analysis utilizing support vector machines. 2009 International Conference on Future BioMedical Information Engineering (FBIE). https://doi.org/10.1109/fbie.2009.5405784
Mahesh S, Rakesh S, Patil VC (2018) Computer aided detection system for lung cancer using computer tomography scans. AIP Conf Proc. https://doi.org/10.1063/1.5032063
McNitt-Gray MF, Armato SG, Meyer CR, Reeves AP, McLennan G, Pais RC, Clarke LP (2007) The lung image database consortium (LIDC) data collection process for nodule detection and annotation. Acad Radiol 14(12):1464–1474. https://doi.org/10.1016/j.acra.2007.07.021
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. https://doi.org/10.1016/j.media.2010.02.004
Murphy K, Van Ginneken B, Schilham A, De Hoop B, Gietema H, Prokop M (2009) A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal 13(5):757–770. https://doi.org/10.1016/j.media.2009.07.001
Niemeijer M, Loog M, Abràmoff MD, Viergever MA, Prokop M, Van Ginneken B (2011) On combining computer-aided detection systems. IEEE Trans Med Imaging 30(2):215–223. https://doi.org/10.1109/tmi.2010.2072789
Orozco HM, Villegas OO, Dominguez HD, Sanchez VG (2013) Lung nodule classification in ct thorax images using support vector machines. 2013 12th Mexican International Conference on Artificial Intelligence. https://doi.org/10.1109/micai.2013.38
Ozekes S, Osman O (2008) Computerized lung nodule detection using 3D feature extraction and learning based algorithms. J Med Syst 34(2):185–194. https://doi.org/10.1007/s10916-008-9230-0
Retico A, Fantacci M, Gori I, Kasae P, Golosio B, Piccioli A, Tangaro S (2009) Pleural nodule identification in low-dose and thin-slice lung computed tomography. Comput Biol Med 39(12):1137–1144. https://doi.org/10.1016/j.compbiomed.2009.10.005
Sahiner B, Chan H, Hadjiiski LM, Cascade PN, Kazerooni EA, Chughtai AR, Attili A (2009) Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size. Acad Radiol 16(12):1518–1530. https://doi.org/10.1016/j.acra.2009.08.006
Setio AA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, Van Ginneken B (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169. https://doi.org/10.1109/tmi.2016.2536809
Setio AA, Traverso A, De Bel T, Berens MS, Bogaard CV, Cerello P, Jacobs C (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal 42:1–13. https://doi.org/10.1016/j.media.2017.06.015
Shapiro L, Kak A (1986) Special issue on current issues and trends in computer vision. Comput Vis Graphics Image Process 36(2–3):137. https://doi.org/10.1016/0734-189x(86)90072-1
Shaukat F, Raja G, Gooya A, Frangi AF (2017) Fully automatic detection of lung nodules in CT images using a hybrid feature set. Med Phys 44(7):3615–3629. https://doi.org/10.1002/mp.12273
Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA: a cancer. J Clin 66(1):7–30. https://doi.org/10.3322/caac.21332
Suárez-Cuenca JJ, Tahoces PG, Souto M, Lado MJ, Remy-Jardin M, Remy J, José Vidal J (2009) Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images. Comput Biol Med 39(10):921–933. https://doi.org/10.1016/j.compbiomed.2009.07.005
Suzuki K, Armato SG, Li F, Sone S, Doi K (2003) Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys 30(7):1602–1617. https://doi.org/10.1118/1.1580485
Tartar A, Kilic N, Akan A (2013) Classification of pulmonary nodules by using hybrid features. Comput Math Methods Med 2013:1–11. https://doi.org/10.1155/2013/148363
Teramoto A, Fujita H, Takahashi K, Yamamuro O, Tamaki T, Nishio M, Kobayashi T (2013) Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: a preliminary study. Int J Comput Assist Radiol Surg 9(1):59–69. https://doi.org/10.1007/s11548-013-0910-y
Valente IR, Cortez PC, Neto EC, Soares JM, De Albuquerque VH, Tavares JM (2016) Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods Progr Biomed 124:91–107. https://doi.org/10.1016/j.cmpb.2015.10.006
Xian G (2010) An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl 37(10):6737–6741. https://doi.org/10.1016/j.eswa.2010.02.067
Yu Y, Zhao H (2006) Enhancement filter for computer-aided detection of pulmonary nodules on thoracic CT images. Sixth International Conference on Intelligent Systems Design and Applications. https://doi.org/10.1109/isda.2006.253783
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shaukat, F., Raja, G., Ashraf, R. et al. Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features. J Ambient Intell Human Comput 10, 4135–4149 (2019). https://doi.org/10.1007/s12652-019-01173-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01173-w