Abstract
Lung cancer is considered as a leading cause of death throughout the globe. Manual interpretation of cancer detection is time consuming and thus increases the death rate. With the help of improvement in medical imaging technology, a computer-aided diagnostics system could be an aid to combat this disease. Automatic segmentation of a region of interest is one of the most challenging problem in medical image analysis. An inaccurate segmentation of solitary pulmonary nodule may lead to an erroneous prediction of the disease. In this paper, we perform a comparative study among the available segmentation techniques, which can automatically segment the solitary pulmonary nodules from high-resolution computed tomography (CT) images and then we propose a computerized lung nodule risk prediction model based on the best segmentation technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sausa, J., Silva, A., Paiva, A., Nunes, R.: Methodology for automatic detection of lung nodules in computerized tomography images. Comput. Methods Progr. Biomed. 98, 1–14 (2009). (Elsevier)
Gurcan, M.N., Berkman, S., Petrick, N., Chan, H.P., Kazerooni, E.A., Cascade, P.N., Hadjiiski, L.: Lung nodule detection on thoracic computed tomographyimages:preliminary evaluationofacomputer-aided diagnosis system. Med. Phys. 29(11), 25522558 (2002)
Gao, T., Sun, X., Wang, Y., Nie, S.: A pulmonary nodules detection method using 3D template matching foundations of intelligent systems. Adv. Intell. Soft Comput. 122, 625–633 (2012)
Ozekes, S., Camurcu, A.Y.: Automatic lung nodule detection using template matching advances in information systems. Lect. Notes Comput. Sci. 4243, 247–253 (2006)
Netto, S., Silva, A., Nunes, R., Gattass, M.: Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput. Biol. Med. 42, 11101121 (2012)
Suarez-Cuenca, J., Tahoces, P.G., Souto, M., Lado, M.: Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images. Comput. Biol. Med. 39, 921–933 (2009). (Elsevier)
Mukherjee, J., Chakrabarti, A., Shaikh, S.H., Kar, M.: Automatic detection and Classifications of Solitary Pulmonary Nodules from CT Images. IEEE Xplore (2014)
Otsu N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Explore (1978)
Bression, X., Esedoglu, S., Vandergheynst, P., Thiran, J-P., Osher, S.: Fast global minimization of active contour/snake model. J. Math. Imaging Vis.
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variance based noise removal algorithms. Phys. D 10(2), 259–268 (1992)
Mumford, J., Shah, J.: Optimal approximations of Piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–685 (1989)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79,1997
Kass, M., Witkin, A., Terzopoulos D., Snakes: Active contour models. Int. J. Comput. Vis. 321–331 (1987)
Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Gonzalez, R.C., Woods, R.E., Digital Image Processing, 3rd edn. Pearson Education (2009)
Chamasemani, F.F., Singh, Y.P.: Multi-class support vector machine (SVM) classifiers-an application in hypothyroid detection and classification. In Proceedings of Bio-Inspired Computing: Theories and Applications, pp. 351–356. IEEE (2011)
Dey, A., Shaikh, S.H., Saeed, K., Chaki, N.: Modified majority voting algorithm towards creating reference image for binarization. Adv. Comput. Netw. Inform. 1, 221–227 (2014)
Roy S., Saha, S., Dey, A., Shaikh, S.H., Chaki N.z;; Performance evaluations of multiple image binarization algorithms using multiple metrices on standard image databases. Proc. Annu. Convention Comput. Soc. India II, 349–360 (2014)
Mukherjee, J., Kundu, R., Chakrabarti, A.: Variability of cobb angle measurement from digital ray image based on different denoising techniques. Int. J. Biomed. Eng. Technol. 16(2), 113–134
Mughal, M.N., Karim, W.: Early Lung Cancer Detection by Classification by Classifying Chest CT Images: A Survey. IEEE (2004)
Manos, D., Sely, J.M., Taylor, J.T., Borgaonkar, J., Roberts, H.C., Mayo, J.R.: The lung reporting and data system (LU-RADS): a proposal for computed tomography screeing. Can Assoc. Radiol. J. 65, 121–134 (2014). (Elsevier)
Iwano, S., Nakamura, T., Kamioka, Y., Ishigaki, T.: Computer-aided diagnosis: a shape classification of pulmonary nodules imaged by high resolution CT Elsevier. Comput. Med. Imaging Graph. 29(2005), 565–570 (2005)
Acknowledgments
We are thankful to the Centre of Excellence in System Biology and Biomedical Engineering (TEQIP II), University of Calcutta for funding this project and Peerless Hospitex Hospital and Research Center Ltd. for providing their valuable lung cancer image database.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this chapter
Cite this chapter
Mukherjee, J., Shaikh, S.H., Kar, M., Chakrabarti, A. (2016). A Comparative Analysis of Image Segmentation Techniques Toward Automatic Risk Prediction of Solitary Pulmonary Nodules. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2650-5_11
Download citation
DOI: https://doi.org/10.1007/978-81-322-2650-5_11
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2648-2
Online ISBN: 978-81-322-2650-5
eBook Packages: EngineeringEngineering (R0)