Abstract
Computer-aided diagnosis (CAD) of mammographic masses is important yet challenging, since masses have large variation in shape and size and are often indistinguishable from surrounding tissue. As an alternative solution, content-based image retrieval (CBIR) techniques can facilitate the diagnosis by finding visually similar cases. However, they still need radiologists to identify suspicious regions in the query case. To overcome the drawbacks of both kinds of methods, we propose a CAD approach that integrates image retrieval with learning-based mass detection. Specifically, a query mammogram is first matched with a database of exemplar masses, getting a series of similarity maps. Then these maps are subtracted by discriminatively learned thresholds to eliminate noise. At last, individual similarity maps are aggregated, and local maxima in the final map are selected as masses. By utilizing a large database, our approach can effectively detect masses despite their variation. Moreover, it bypasses the identification of suspicious regions by radiologists. Experiments are conducted on 500 mammograms randomly selected from the digital database for screening mammography (DDSM) using receiver operating characteristic (ROC) analysis. The proposed approach achieves a promising ROC area index A z = 0.91, and outperforms two traditional classifier-based CAD methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Birdwell, R.L., Ikeda, D.M., O’Shaughnessy, K.F., Sickles, E.A.: Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection. Radiology 219(1), 192–202 (2001)
Caicedo, J.C., Cruz, A., Gonzalez, F.A.: Histopathology image classification using bag of features and kernel functions. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds.) AIME 2009. LNCS, vol. 5651, pp. 126–135. Springer, Heidelberg (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. IEEE CVPR, pp. 886–893 (2005)
Ganesan, K., Acharya, U.R., Chua, C.K., Min, L.C., Abraham, K.T., Ng, K.H.: Computer-aided breast cancer detection using mammograms: A review. IEEE Rev. Biomed. Eng. 6, 77–98 (2013)
Heath, M., Bowyer, K., Kopans, D., Kegelmeyer Jr., W.P., Moore, R., Chang, K., Munishkumaran, S.: Current status of the digital database for screening mammography. In: Digital Mammography, pp. 457–460. Springer, Netherlands (1998)
Jiang, M., Zhang, S., Liu, J., Shen, T., Metaxas, D.N.: Computer-aided diagnosis of mammographic masses using vocabulary tree-based image retrieval. In: Proc. IEEE ISBI (2014)
Kumar, A., Kim, J., Cai, W., Fulham, M., Feng, D.: Content-based medical image retrieval: A survey of applications to multidimensional and multimodality data. J. Digit. Imaging 26(6), 1025–1039 (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Molinara, M., Marrocco, C., Tortorella, F.: A boosting-based approach to refine the segmentation of masses in mammography. In: Petrosino, A. (ed.) ICIAP 2013, Part II. LNCS, vol. 8157, pp. 572–580. Springer, Heidelberg (2013)
Moura, D.C., Guevara-López, M.Á.: An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int. J. Comput. Assist. Radiol. Surg. 8(4), 561–574 (2013)
Müller, H., Michoux, N., Bandon, D., Geissbühler, A.: A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. Int. J. Med. Inform. 73(1), 1–23 (2004)
Nemoto, M., Shimizu, A., Kobatake, H., Takeo, H., Nawano, S.: Study on cascade classification in abnormal shadow detection for mammograms. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 324–331. Springer, Heidelberg (2006)
Oliver, A., Freixenet, J., Martí, J., Pérez, E., Pont, J., Denton, E.R.E., Zwiggelaar, R.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14(2), 87–110 (2010)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Shen, X., Lin, Z., Brandt, J., Avidan, S., Wu, Y.: Object retrieval and localization with spatially-constrained similarity measure and k-nn re-ranking. In: Proc. IEEE CVPR, pp. 3013–3020 (2012)
Shen, X., Lin, Z., Brandt, J., Wu, Y.: Detecting and aligning faces by image retrieval. In: Proc. IEEE CVPR, pp. 3460–3467 (2013)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proc. IEEE ICCV, pp. 1470–1477 (2003)
Tang, J., Rangayyan, R.M., Xu, J., El Naqa, I., Yang, Y.: Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Trans. Inf. Technol. Biomed. 13(2), 236–251 (2009)
Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE CVPR, pp. I–511–I–518 (2001)
Zhang, X., Liu, W., Zhang, S.: Mining histopathological images via hashing-based scalable image retrieval. In: Proc. IEEE ISBI (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Jiang, M., Zhang, S., Metaxas, D.N. (2014). Detection of Mammographic Masses by Content-Based Image Retrieval. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-10581-9_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10580-2
Online ISBN: 978-3-319-10581-9
eBook Packages: Computer ScienceComputer Science (R0)