Abstract
Breast lesion boundaries have been mostly extracted by using conventional approaches as a previous step in the development of computer-aided diagnosis systems. Among these, region growing is a frequently used segmentation method. To make the segmentation completely automatic, most of the region growing methods incorporate automatic selection of the seed points. This paper proposes a new automatic seed placement algorithm for breast lesion segmentation on ultrasound images by means of assigning the probability of belonging to a lesion for every pixel depending on intensity, texture and geometrical constraints. The proposal has been evaluated using a set of sonographic breast images with accompanying expert-provided ground truth, and successfully compared to other existing algorithms.
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References
Drukker, K., Giger, M.L., Kupinski, K.M.A., Vyborny, C.J., Mendelson, E.B.: Computerized lesion detection on breast ultrasound. Medical Physics 29(7), 1438–1446 (2002)
Kupinski, M.A., Giger, M.L.: Automated seeded lesion segmentation on digital mammograms. IEEE Transactions on Medical Imaging 17(4), 510–517 (1998)
Madabhushi, A., Metaxas, D.: Automatic boundary extraction of ultrasonic breast lesions. In: Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 601–604 (2002)
Massich, J., Meriaudeau, F., Pérez, E., Martí, R., Oliver, A., Martí, J.: Lesion Segmentation in Breast Sonography. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 39–45. Springer, Heidelberg (2010)
Moore, S.K.: Better breast cancer detection. IEEE Spectrum 38(5), 50–54 (2001)
Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: A survey. IEEE Transactions on Medical Imaging 25(8), 987–1010 (2006)
Shan, J., Cheng, H.D., Wang, Y.: A novel automatic seed point selection algorithm for breast ultrasound images. In: 19th International Conference on Pattern Recognition (2008)
Sivaramakrishna, R., Powell, K.A., Lieber, M.L., Chilcote, W.A., Shekhar, R.: Texture analysis of lesions in breast ultrasound images. Computerized Medical Imaging and Graphics 26(5), 303–307 (2002)
Stavros, A.T., Rapp, C.L., Parker, S.H.: Breast ultrasound. Lippincott Williams & Wilkins (2004)
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23(7), 903–921 (2004)
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Massich, J. et al. (2012). Automatic Seed Placement for Breast Lesion Segmentation on US Images. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_40
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DOI: https://doi.org/10.1007/978-3-642-31271-7_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31270-0
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