Abstract
A semi-automatic method has been developed which segments the prostate in slices of Magnetic Resonance Imaging (MRI) data. The developed approach exploits the characteristics of the anatomical shape of the prostate when represented in a polar transform space. Simple techniques, such as line detection and non-maximum suppression, are used to track the boundary of the prostate. The initial results, based on a small set of data, indicate a good correlation with expert based manual segmentation.
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References
Hayward, S.W., Cunha, G.R.: The prostate: Development and physiology. Radiologic Clinics of North America 38(1), 1–15 (2000)
Yu, K.K., Hricak, H.: Imaging prostate cancer. Radiologic Clinics of North America 38(1), 59–87 (2000)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 30(2), 117–154 (1998)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision. Chapman and Hall Publishing, Boca Raton (1993)
van Ginneken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: Active shape model segmentation with optimal features. IEEE Transactions on Medical Imaging 21(8), 924–933 (2002)
Knoll, C., Alcaniz, M., Grau, V., Monserrat, C., Juan, M.C.: Outlining of the prostate using snakes with shape restrictions based on the wavelet transform. Pattern Recognition 32(10), 1767–1781 (1999)
Ghanei, A., Soltanian-Zadeh, H., Ratkewicz, A., Yin, F.F.: A three-dimensional deformable model for segmentation of human prostate from ultrasound images. Medical Physics 28(10), 2147–2153 (2001)
Mazonakis, M., Damilakis, J., Varveris, H., Prassopoulos, P., Gourtsoyiannis, N.: Image segmentation in treatment planning for prostate cancer using the region growing technique. British Journal of Radiology 74(879), 243–248 (2001)
Cootes, T.F., Hill, A., Taylor, C.J.: The use of active shape models for locating structures in medical images. Image and Vision Computing 12(6), 355–365 (1994)
Bharatha, A., Hirose, M., Hata, N., Warfield, S.K., Ferrant, M., Zou, K.H., Suarez-Santana, E., Ruiz-Alzola, J., D’Amico, A., Cormack, R.A., Kikinis, R., Jolesz, F.A., Tempany, C.M.C.: Evaluation of three-dimensional finite element-based deformable registration of pre- and intraoperative prostate imaging. Medical Physics 28(12), 2551–2560 (2001)
Fei, B.W., Wheaton, A., Lee, Z.H., Duerk, J.L., Wilson, D.L.: Automatic mr volume registration and its evaluation for the pelvis and prostate. Phys. Med. Biol. 47(5), 823–838 (2002)
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Zwiggelaar, R., Zhu, Y., Williams, S. (2003). Semi-automatic Segmentation of the Prostate. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_128
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DOI: https://doi.org/10.1007/978-3-540-44871-6_128
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