Abstract
This paper presents a generic strategy to facilitate the segmentation of anatomical structures in medical images. The segmentation is performed using an adapted PDM by fuzzy c-means classification, which also uses the fuzzy decision to evolve PDM into the final contour. Furthermore, the fuzzy reasoning exploits \(\it{a}\) \(\it{priori}\) statistical information from several knowledge sources based on histogram analysis and the intensity values of the structures under consideration. The fuzzy reasoning is also applied and compared to a geometrical active contour model (or level set). The method has been developed to assist clinicians and radiologists in conformal RTP. Experimental results and their quantitative validation to assess the accuracy and efficiency are given segmenting the bladder on CT images. To assess precision, results are also presented in CT images with added Gaussian noise. The fuzzy-snake is free of parameter and it is able to properly segment the structures by using the same initial spline curve for a whole study image-patient set.
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
Duncan, J.S., Ayache, N.: Medical Image Analysis: Progress over Two Decades and the Challenges Ahead. IEEE Trans. on PAMI 22, 85–106 (2000)
Lee, C., Chung, P., Tsai, H.: Identifying Multiple Abdominal Organs from CT Image Series Using a Multimodule Contextual Neural Network and Spatial Fuzzy Rules. IEEE Trans. on Information Technology in Biomedicine 7 (3), 208–217 (2003)
Purdy, J.A.: 3D Treatment Planning and Intensity-Modulated Radiation Therapy. Oncology 13, 155–168 (1999)
Haas, O.: Radiotherapy Treatment Planning, New System Approaches. Springer, Heidelberg (1998)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput.Vis. 14 (26), 321–331 (1988)
Yu, Z., Bajaj, C.: Image Segmentation Using Gradient Vector Diffusion and Region Merging. In: IEEE Int. Conference on Pattern Recognition (2002)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape Modeling with Front Propagation: A Level Set Approach. IEEE Trans. on PAMI 17, 158–175 (1995)
Wang, H., Ghosh, B.: Geometric Active Deformable Models in Shape Modeling. IEEE Trans. on Image Processing 9 (2), 302–308 (2000)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. Int. J. Comput. Vis 22 (1), 61–79 (1997)
Ray, N., Havlicek, J., Acton, S.T., Pattichis, M.: Active Contour Segmentation Guided by AM-FM Dominant Componente Analysis. In: IEEE Int. Conference on Image Processing, pp. 78–81 (2001)
Solaiman, B., Debon, B., Pipelier, R., Cauvin, F., Roux, J.-M.: Information Fusion: Application to Data and Model Fusion for Ultrasound Image Segmentation. IEEE Trans. on BioMedical Engineering 46 (10), 1171–1175 (1999)
Mohamed, N.: A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Trans. on Medical Imaging 21 (3), 193–200 (2002)
Bueno, G., Fisher, M., Burnham, K., Haas, O.: Automatic segmentation of clinical structures for RTP: Evaluation of a morphological approach. In: MIAU Int. Conference. U.K. 22, pp. 73–76 (2001)
Ivins, J., Porrill, J.: Active Region Models for Segmenting Medical Images. IEEE Trans. on Image Processing, 227–231 (1994)
Chalana, V., Linker, D.T.: A Multiple Active Contour Model for Cardiac Boundary Detection on Echocardiographic Sequences. IEEE Trans. on Medical Imaging 153, 290–298 (1996)
Udupa, J.K., LeBlancb, V.R., Schmidt, H., Ying, Y.: Methodology for Evaluating Image Segmentation Algorithms. In: Proceed. of SPIE, vol. 4684, pp. 266–277 (2002)
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
Bueno, G., Martínez-Albalá, A., Adán, A. (2004). Fuzzy-Snake Segmentation of Anatomical Structures Applied to CT Images. 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_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-30126-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
Online ISBN: 978-3-540-30126-4
eBook Packages: Springer Book Archive