Abstract
Detection of lesions in ultrasound imaging typically requires human analysis due to their complexity. Hence, computerized lesion detection methods could be used to help radiologists in this process due to the fact that an early detection reduces the death rate caused by breast cancer. In this paper we propose a first experiment of a feasibility study for adapting a generic object detection technique, Deformable Part Models (DPM), to detect lesions in breast US images without any kind of human supervision. This technique has been evaluated in different topics obtaining prominent results. Hence, we propose a first assessment of this methodology applied to lesion detection in US images. We used a data-set composed by 50 images, all from different patients (18 malignant lesions, 32 benign lesions and 50 healthy tissue regions). In terms of quantitative results for lesion detection, our proposal obtains a sensitivity of 82% with 0.51 false-positive detections per image and an A z value of 0.96, which proves the feasibility of the proposal.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alvarenga, A., Infantosi, A., Pereira, W., Azevedo, C.: Assessing the combined performance of texture and morphological parameters in distinguishing breast tumors in ultrasound images. Medical Physics 39(12), 7350–7358 (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 886–893 (2005)
Drukker, K., Giger, M.L., Horsch, K., Kupinski, M.A., Vyborny, C.J., Mendelson, E.B.: Computerized lesion detection on breast ultrasound. Medical Physics 29(7), 1438–1446 (2002)
Drukker, K., Giger, M.L., Metz, C.E.: Robustness of computerized lesion detection and classification scheme across different breast us platforms. Radiology 237(3), 834–840 (2005)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2010 (VOC 2010) (2010), Results http://www.pascal-network.org/challenges/VOC/voc2010/workshop/index.html
Fawcett, T.: An introduction to roc analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)
Hao, Z., Wang, Q., Seong, Y.K., Lee, J.-H., Ren, H., Kim, J.-Y.: Combining CRF and multi-hypothesis detection for accurate lesion segmentation in breast sonograms. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 504–511. Springer, Heidelberg (2012)
Horsch, K., Giger, M.L., Venta, L.A., Vyborny, C.J.: Computerized diagnosis of breast lesions on ultrasound. Medical Physics 29(2), 157–164 (2002)
Kutay, M.A., Petropulu, A.P., Piccoli, C.W.: Breast tissue characterization based on modeling of ultrasonic echoes using the power-law shot noise model. Pattern Recognition Letters 24(4-5), 741–756 (2003)
Mogatadakala, K.V., Donohue, K.D., Piccoli, C.W., Forsberg, F.: Detection of breast lesion regions in ultrasound images using wavelets and order statistics. Medical Physics 33(4), 840–849 (2006)
Shan, J., Cheng, H., Wang, Y.: Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound in Medicine and Biology 38(2), 262–275 (2012)
Stavros, A., Thickman, D., Rapp, C., Dennis, M., Parker, S., Sisney, G.: Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions. Radiology 196(1), 123–134 (1995)
Yap, M.H., Edirisinghe, E.A., Bez, H.E.: A novel algorithm for initial lesion detection in ultrasound breast images. Journal of Applied Clinical Medical Physics 9(4), 181–199 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pons, G., Martí, R., Ganau, S., Sentís, M., Martí, J. (2013). Feasibility Study of Lesion Detection Using Deformable Part Models in Breast Ultrasound Images. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-38628-2_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
eBook Packages: Computer ScienceComputer Science (R0)