Abstract
A statistically based segmentation method was applied to the recognition of organs in abdominal CT scans. To incorporate prior knowledge of anatomical structure, a stochastic model was used that represented abdominal geometry in three dimensions. Properties of each tissue class with respect to X-ray imaging were modeled by mean grey-value distributions. Twelve different tissues were labeled simultaneously. Deterministic maximization of the a posteriori distribution as well as stochastic optimization by simulated annealing were both applied. Mean segmentation results were determined for a set of 18 scan sequences, using a set of reference contours designated by a radiologist as ground truth. Results appeared to be somewhat better using simulated annealing. The proposed segmentation method, which is fast and fully automatic, seems sufficiently accurate for many clinical applications, such as determination of relative organ volumes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Besag JE (1974) Spatial interaction and the statistical analysis of lattice systems. Journal of Royal Statistical Society Series B 36:192–236
Besag JE (1986) On the statistical analysis of dirty pictures. Journal of Royal Statistical Society, Series B 48:259–302
Dubes RC, Jain AK (1989) Random field models in image analysis. Journal of Applied Statistics 16(2):131–164
Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis Machine Intelligence 6:721–741
Hansen FR, Elliot H (1982) Image segmentation using simple Markov random fields. Computer Graphics Image Processing 20:101–132
Karssemeijer N (1990) A relaxation method for image segmentation using a spatially dependent stochastic model. Pattern Recognition Letters 11:13–23
Karssemeijer N, van Erning LJ Th O, Eikjman EGJ (1988) Recognition of organs in CT-image sequences: A model guided approach. Computer and Biomedical Research 21:434–448
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Marroquin J, Mitter S, Poggio T (1987) Probabilistic solution of ill-posed problems in computational vision. Journal of the American Statistical Association 82(397):76–89
Murray DW, Buxton BF (1987) Scene segmentation from visual motion using global optimization. IEEE Transactions on Pattern Analysis Machine Intelligence 9(2):220–228
Newell JA, Sokolowska (1986) Model based recognition of CT scan images. In: Proceedings of Medinfo 1986. Elsevier, Amsterdam.
Schlegel W (1987) Progress and trends in computer assisted radiation therapy. In: Proceedings of Computer Assisted Radiology 1987. Springer-Verlag, Berlin
Shani U (1983) Understanding three-dimensional images: Recognition of abdominal anatomy from CAT scans. UMI Research Press, Ann Arbor
Tronnier U, Wolff KD, Trittmacher (1989) A 3-D surgical planning system and its clinical applications. In: Proceedings of Computer Assisted Radiology 1989. Springer-Verlag, Berlin
Udupa JK (1986) Computerized surgical planning: Current capabilities and medical needs. In: Proceedings of SPIE, Vol. 626, Medicine XIV/PACS IV:474–481
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Karssemeijer, N. A statistical method for automatic labeling of tissues in medical images. Machine Vis. Apps. 3, 75–86 (1990). https://doi.org/10.1007/BF01212192
Issue Date:
DOI: https://doi.org/10.1007/BF01212192