Abstract
Extracting the shape of the gallbladder from an ultrasonography (US) image allows superfluous information which is immaterial in the diagnostic process to be eliminated. In this project an active contour model was used to extract the shape of the gallbladder, both for cases free of lesions, and for those showing specific disease units, namely: lithiasis, polyps and changes in the shape of the organ, such as folds or turns of the gallbladder. The approximate shape of the gallbladder was found by applying the motion equation model. The tests conducted have shown that for the 220 US images of the gallbladder, the area error rate (AER) amounted to 18.15%.
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References
Aarnink, R.G., Pathak, S.D., de la Rosette, J.J., Debruyne, F.M., Kim, Y., et al.: Edge detection in prostatic ultrasound images using integrated edge maps. Ultrasonics 36, 635–642 (1998)
Bodzioch, S.: Information reduction in digital image and its influence on the improvement of recognition process. Automatics, Semi-Annual Journal of the AGH University of Science an Technology 8(2), 137–150 (2004)
Ciecholewski, M., Dȩbski, K.: Automatic Segmentation of the Liver in CT Images Using a Model of Approximate Contour. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 75–84. Springer, Heidelberg (2006)
Ciecholewski, M., Ogiela, M.: Automatic Segmentation of Single and Multiple Neoplastic Hepatic Lesions in CT Images. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 63–71. Springer, Heidelberg (2007)
Cohen, L.D., Cohen, I.: Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1131–1147 (1993)
Cvancarova, M., Albregtsen, T.F., Brabrand, K., Samset, E.: Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF. International Congress Series (ICS), pp. 218–223 (2005)
Hamou, A.K., Osman, S., El-Sakka, M.R.: Carotid Ultrasound Segmentation Using DP Active Contours. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 961–971. Springer, Heidelberg (2007)
Kass, M., Witkin, A., Terauzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1988)
Leymarie, F., Levine, M.D.: Simulating the Grassfire Transform using an Active Contour Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(1), 56–75 (1992)
Neuenschwander, W., Fua, P., Kuebler, O.: From Ziplock Snakes to Velcro Surfaces, Automatic Extraction of Man Made Objects from Aerial and Space Images, pp. 105–114. Birkhaeuser Verlag, Basel (1995)
Richard, W.D., Keen, C.G.: Automated texture-based segmentation of ultrasound images of the prostate. Comput. Med. Imaging Graph. 20(3), 131–140 (1996)
Roberts, M.G., Cootes, T.F., Adams, J.E.: Automatic segmentation of lumbar vertebrae on digitised radiographs using linked active appearance models. In: Proc. Medical Image Understanding and Analysis, vol. 2, pp. 120–124 (2006)
Schilling, R.J., Harris, S.L.: Applied numerical methods for engineers. Brooks/Cole Publishing Com., Pacific Grove (2000)
Szczypiński, P., Strumiłło, P.: Application of an Active Contour Model for Extraction of Fuzzy and Broken Image Edges. Machine Graphics & Vision 5(4), 579–594 (1996)
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Ciecholewski, M. (2010). Gallbladder Boundary Segmentation from Ultrasound Images Using Active Contour Model. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_8
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DOI: https://doi.org/10.1007/978-3-642-15381-5_8
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