Abstract
This paper presents a method for the initial detection of ductal structures within 3D ultrasound images using second-order shape measurements. The desire to detect ducts is motivated in a number of way, principally as step in the detection and assessment of ductal carcinoma in-situ. Detection is performed by measuring the variation of the local second-order shape from a prototype shape corresponding to a perfect tube. We believe this work is the first demonstration of the ability to detect sections of duct automatically in ultrasound images. The detection is performed with a view to employing vessel tracking method to delineate the full ductal structure.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Teboul, M., Halliwell, M.: Atlas of ultrasound and ductal echography of the breast. Blackwell Science Ltd., Oxford (1995)
Moskalik, A., Carson, P., Roubidoux, M.: 3D tracking and display of mammary ducts. In: Proc. IEEE Ultrasonics symposium 1995., vol. 1636, pp. 1167–1170 (1995)
Donohue, K., Huang, L., Genis, V., Forsberg, F.: Duct size detection and estimation in breast tissue. In: 1999 IEEE ultrasonics symposium, pp. 1353–1356 (1999)
Nani, M., Piperno, G.: Il sistena duttale mammario. aspetti ecografici. Radiol. Med. 83, 745–750 (1992)
Going, J., Moffat, D.: Escaping from flatland: clinical and biological aspects of human mammary duct anatomy in three dimensions. Journal of Pathology 203, 538–544 (2004)
Ohtake, T., Kimijima, I., Fukushima, T., Yasuda, M., Sekikawa, K., Takenoshita, S., Abe, R.: Computer-assisted complete three-dimensional reconstruction of the mammary ductal/lobular systems. Cancer 19, 2263–2272 (2001)
Aylward, S., Jomier, J., Weeks, S., Bullitt, E.: Registration and analysis of vascular images. Int. J. Comp. Vision 55, 123–138 (2003)
Ramsay, D., Kent, J., Owens, R., Hartmann, P.: Ultrasound imaging of milk ejection in the breast of lactating women. Pediatrics 113, 361–367 (2004)
Moffat, D., Going, J.: Three dimensional anatomy of complete duct systems in human breast: pathological and developmental implications. Journal of Clinical Pathology 49, 48–52 (1996)
Wilson, D., Noble, J.: An adaptive segmentation algorithm for time-of-flight mra data. IEEE Trans. Med. Imag. 18, 938–945 (1999)
Frangi, A., Niessen, W., Vincken, K., Viergever, M.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Danielsson, P.E., Lin, Q., Ye, Q.Z.: Efficient detection of second-degree variations in 2D and 3D images. Journal of Visual Communication and Image Representation 12, 255–305 (2001)
Koenderink, J., van Doorn, A.: Surface shape and curvature scales. Image Vision Comput. 10, 557–564 (1992)
Lin, Q., Danielsson, P.-E.: Stenosis detection using a new shape space for second order 3D-variations. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 388–394. Springer, Heidelberg (2001)
Sato, Y., Bhalerao, A., Nakajima, S., Shiraga, N., Tamura, S., Kikinis, R.: Tissue classification based on 3D local intensity structure for volume rendering. IEEE Transactions on visualization and computer graphics 6, 160–180 (2000)
Aylward, S., Bullitt, E.: Initialization, noise, singularities and scale in hieght ridge traversal for tubular object centerline extraction. IEEE Trans. Med. Imag. 21, 61–75 (2002)
Lorigo, L., Faugeras, O., Grimson, W., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.: Curves: Curve evolution for vessel segmentation. Medical Image Analysis 5, 195–206 (2001)
Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model based detection of tubular structures in 3D images. Computer Vision and Image Understanding 80, 130–171 (2000)
Fridman, Y., Pizer, S., Aylward, S., Bullitt, E.: Segmenting 3D branching tubular structures using cores. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 570–577. Springer, Heidelberg (2003)
Stetten, G., Pizer, S.: Medial-node models to identify and measure objects in real-time 3-D echocardiography. IEEE Trans. Med. Imag. 18, 1025–1034 (1999)
ter Haar Romeny, B.: 6. In: Front-end vision and multi-scale image analysis, pp. 132–133. Kluwer Academic Publishers, Dordrecht (2003)
Shipley, J., Duck, F., Thomas, B.: Registration of ultrasound breast images acquired from a conical geometry. In: Proc. Medical Image Understanding and Analysis 2003, pp. 45–48 (2003)
Shipley, J., Duck, F., Goddard, D., Hillman, M., Halliwell, M., Jones, M., Thomas, B.: Automated quantitative volumetric breast ultrasound data acquisition system. Ultrasound in Medicine and Biology 31, 905–917 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gooding, M.J., Mellor, M., Shipley, J.A., Broadbent, K.A., Goddard, D.A. (2005). Automatic Mammary Duct Detection in 3D Ultrasound. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_54
Download citation
DOI: https://doi.org/10.1007/11566465_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)