Abstract
Mammogram images are broadly categorized into two types: carniocaudal (CC) view and mediolateral oblique (MLO) view. In this paper, we study the effect of different image views for mammogram mass classification. For the experiments, we consider a dataset of 328 CC view images and 334 MLO view images (almost equal ratio) from a publicly available film mammogram image dataset [3]. First, features are extracted using a novel radon-wavelet based image descriptor. Then an extreme learning machine (ELM) based classification technique is applied and the performance of five different ELM kernels are compared: sigmoidal, sine, triangular basis, hard limiter and radial basis function. Performances are reported in terms of three important statistical measures namely, sensitivity or true positive rate (TPR), specificity or false negative rate (SPC) and recognition accuracy (ACC). Our experimental outcome for the present setup is two-fold: (i) CC view performs better then MLO for mammogram mass classification, (ii) hard limiter is the best ELM kernel for this problem.
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
http://www.ntu.edu.sg/home/egbhuang/. Accessed 01 Mar 2017
Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Convolutional neural networks for mammography mass lesion classification. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 797–800. IEEE (2015)
Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed. 127, 248–257 (2016)
Belkasim, S.O., Shridhar, M., Ahmadi, M.: Pattern recognition with moment invariants: a comparative study and new results. Pattern Recognit. 24(12), 1117–1138 (1991)
Constantinidis, A., Fairhurst, M.C., Rahman, A.F.R.: A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms. Pattern Recognit. 34(8), 1527–1537 (2001)
Deans, S.: Applications of the Radon Transform, p. 2. Wiley, New York (1983)
Dhawan, A.P., Chitre, Y., Kaiser-Bonasso, C.: Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Trans. Med. Imaging 15(3), 246–259 (1996)
Dua, S., Singh, H., Thompson, H.W.: Associative classification of mammograms using weighted rules. Expert. Syst. Appl. 36(5), 9250–9259 (2009)
Eltoukhy, M.M., Faye, I., Samir, B.B.: A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput. Biol. Med. 40(4), 384–391 (2010)
Ferreira, C.B.R., Borges, D.L.: Analysis of mammogram classification using a wavelet transform decomposition. Pattern Recognit. Lett. 24(7), 973–982 (2003)
Haralick, R.M., Shanmugam, K., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Huang, G.B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6(3), 376–390 (2014)
Huang, G.B., Bai, Z., Kasun, L.L.C., Vong, C.M.: Local receptive fields based extreme learning machine. IEEE Comput. Intell. Mag. 10(2), 18–29 (2015)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(2), 513–529 (2012)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Mazo, C., Alegre, E., Trujillo, M., González-Castro, V.: Tissues classification of the cardiovascular system using texture descriptors. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 123–132. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_11
Moura, D.C., López, M.A.G.: An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int. J. Comput. Assist. Radiol. Surg. 8(4), 561–574 (2013)
Obaidullah, S.M., Ahmed, S., Goncalves, T., Rato, L.: RMID: a novel and efficient image descriptor for mammogram mass classification. In: 3rd Conference on Information Technology, Systems Research and Computational Physics (2018, accepted)
Obaidullah, S.M., Bose, A., Mukherjee, H., Santosh, K., Das, N., Roy, K.: Extreme learning machine for handwritten indic script identification in multiscript documents. J. Electron. Imaging 27(5), 051214 (2018)
http://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/. Accessed 01 Mar 2018
O’Neil, A., Shepherd, M., Beveridge, E., Goatman, K.: A comparison of texture features versus deep learning for image classification in interstitial lung disease. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 743–753. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_65
Pisano, E.D., et al.: Diagnostic accuracy of digital versus film mammography: exploratory analysis of selected population subgroups in DMIST. Radiology 246(2), 376–383 (2008)
Ramos-Pollán, R., et al.: Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4), 2259–2269 (2012)
Rashed, E.A., Ismail, I.A., Zaki, S.I.: Multiresolution mammogram analysis in multilevel decomposition. Pattern Recognit. Lett. 28(2), 286–292 (2007)
Sahiner, B., Chan, H.P., Petrick, N., Helvie, M.A., Hadjiiski, L.M.: Improvement of mammographic mass characterization using spiculation measures and morphological features. Med. Phys. 28(7), 1455–1465 (2001)
Skaane, P., Hofvind, S., Skjennald, A.: Randomized trial of screen-film versus full-field digital mammography with soft-copy reading in population-based screening program: follow-up and final results of Oslo II study. Radiology 244(3), 708–717 (2007)
Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2016)
Wang, D., Shi, L., Heng, P.A.: Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing 72(13–15), 3296–3302 (2009)
Yu, S., Guan, L.: A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Trans. Med. Imaging 19(2), 115–126 (2000)
Acknowledgements
The first and second author of this paper are thankful to Erasmus Leader project funded by European Commission for their post-doctoral and doctoral research study at University of Évora, Portugal. The first author also acknowledges his employer Aliah University for granting study leave for post-doctoral research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Obaidullah, S.M., Ahmed, S., Gonçalves, T. (2019). Effect of Image View for Mammogram Mass Classification – An Extreme Learning Based Approach. In: Barneva, R., Brimkov, V., Kulczycki, P., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2018. Lecture Notes in Computer Science(), vol 10986. Springer, Cham. https://doi.org/10.1007/978-3-030-20805-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-20805-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20804-2
Online ISBN: 978-3-030-20805-9
eBook Packages: Computer ScienceComputer Science (R0)