Abstract
A Point Distribution Model (PDM) of the prostate has been constructed and used to automatically outline the contour of the gland in transurethral ultrasound images. We developed a new, two stage, method: first the PDM is fitted, using a multi-population genetic algorithm, to a binary image produced from Bayesian pixel classification. This contour is then used during the second stage to seed the initial population of a simple genetic algorithm, which adjusts the PDM to the prostate boundary on a grey level image. The method is able to find good approximations of the prostate boundary in a robust manner. The method and its results on 4 prostate images are reported.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arambula Cosio, F., Davies, B.L.: Automated prostate recognition: A key process of clinically effective robotic prostatectomy. Med. Biol. Eng. Comput. 37, 236–243 (1999)
Aarnik, R.G., Pathak, S.D., de la Rosette, J.J.M.C.H., Debruyne, F.M.J., Kim, Y., Wijkstra, H.: Edge detection in prostatic ultrasound images using integrated edge maps. Ultrasonics 36, 635–642 (1998)
Liu, Y.J., Ng, W.S., Teo, M.Y., Lim, H.C.: Computerised prostate boundary estimation of ultrasound images using radial bas-relief method. Med. Biol. Eng. Comput. 35, 445–454 (1997)
Dinggang, S., Yiqiang, Z., Christos, D.: Segmentation of Prostate Boundaries From Ultrasound Images Using Statistical Shape Model. IEEE Trans. Med. Imag. 22(4), 539–551 (2003)
Pathak, S.D., Chalana, V., Haynor, D.R., Kim, Y.: Edge-guided boundary delineation in prostate ultrasound images. IEEE Trans. Med. Ima. 19(12), 1211–1219 (2000)
Gong, L., Pathak, S.D., Haynor, D.R., Cho, P.S., Kim, Y.: Parametric shape modelling using deformable superellipses for prostate segmentati. IEEE Trans. Med. Imag. 23(3), 340–349 (2004)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models -Their training and application. Comput. Vision Image Understanding. 61, 38–59 (1995)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)
Golberg, D.E.: Genetic algorithms in search optimization and machine learning. Addison-Wesley, London (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cosío, F.A. (2004). Robust Fitting of a Point Distribution Model of the Prostate Using Genetic Algorithms. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-30126-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
Online ISBN: 978-3-540-30126-4
eBook Packages: Springer Book Archive