Abstract
Content-based image retrieval (CBIR) has recently emerged as a promising method to assist radiologists in diagnosing mammographic masses by displaying pathologically similar cases. In this paper, a CBIR system using curvelet transform and kernel principal component analysis (KPCA) is proposed. Thanks to its improved direction and edge representation abilities, curvelet transform first provides desirable mammographic features. Once the region of interest (ROI) is curvelet transformed, the KPCA is then applied and the first components are used as descriptors. Bearing in mind that neighbor points are the most important but faraway points may contain useful information in mammogram retrieval, we propose a new mixed kernel that overcomes the shortcoming of Gaussian kernels and emphasis neighbor points without neglecting faraway ones. The proposed mixed kernel is a mixture of two gaussian kernels with high and low sigma values. Experiments performed on a large dataset of mammograms showed the superiority of the proposed kernel over single gaussian kernels.
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
Similar content being viewed by others
References
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D.M., Forman, D., Bray, F.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)
Nelson, H.D., Tyne, K., Naik, A., Bougatsos, C., Chan, B.K., Humphrey, L.: Screening for breast cancer: an update for the US Preventive Services Task Force. Ann. Intern. Med. 151(10), 727–737 (2009)
Dinnes, J., Moss, S., Melia, J., Blanks, R., Song, F., Kleijnen, J.: Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: findings of a systematic review. Breast 10(6), 455–463 (2001)
Astley, S.M., Gilbert, F.J.: Computer-aided detection in mammography. Clin. Radiol. 59(5), 390–399 (2004)
Wei, C.-H., Li, C.-T.: Calcification descriptor and relevance feedback learning algorithms for content-based mammogram retrieval. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 307–314. Springer, Heidelberg (2006)
Bedo, M., dos Santos, D., Ponciano, M., de Azevedo, P., Traina, C.: Endowing a content-based medical image retrieval system with perceptual similarity using ensemble strategy. J. Digit. Imaging 29(1), 22–37 (2016)
Jiang, M., Zhang, S., Li, H., Metaxas, D.N.: Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Trans. Biomed. Eng. 62(2), 783–792 (2015)
Dhahbi, S., Barhoumi, W., Zagrouba, E. Multi-view score fusion for content-based mammogram retrieval. In: 8th International Conference on Machine Vision, pp. 987515-1–987515-6 (2015)
Dhahbi, S., Barhoumi, W., Zagrouba, E.: Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput. Biol. Med. 64, 79–90 (2015)
Gardezi, S., Faye, I., Eltoukhy, M.: Analysis of mammogram images based on texture features of curvelet Sub-bands. In: 5th International Conference on Graphic and Image Processing (2014)
Gedik, N., Atasoy, A.: A computer-aided diagnosis system for breast cancer detection by using a curvelet transform. Turkish J. Electr. Eng. Comput. Sci. 21(4), 1002–1014 (2013)
Candes, E.J., Donoho, D.L.: Curvelets: a surprisingly effective nonadaptive representation for objects with edges. Stanford University, Dept of Statistics, California, pp. 1–10 (2000)
Starck, J.L., Cands, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)
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)
Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, W., Moore, R., Chang, K., Munishku-maran, S.: Current status of the digital database for screening mammography. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds.) Digital Mammography. Computational Imaging and Vision, pp. 457–460. Springer, New York (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Dhahbi, S., Barhoumi, W., Zagrouba, E. (2016). Content-Based Mammogram Retrieval Using Mixed Kernel PCA and Curvelet Transform. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-48680-2_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48679-6
Online ISBN: 978-3-319-48680-2
eBook Packages: Computer ScienceComputer Science (R0)