Abstract
Automatic segmentation of organs at risk in head Magnetic Resonance Images (MRI) is a challenging task in medical image analysis. This operation is fundamental for radiotherapy treatment planning: accurate delineation of critical structures allow calibrating the radiation beam in order to hit tumour cells and preserve sane tissues, consuming a time of much lower than a radiation oncologist. In this paper we analyze the properties of head MRI and of their OARs and propose an algorithm that exploits the knowledge implied in an atlas, represented by a labelled medical image, and uses a modified version of Gradient Vector Flow Snake endowed with a parameters automatic tuning mechanism system based on Fourier Descriptors. The comparison of this method with the other traditional algorithms based on active contours showed a remarkable increase of performance.
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
Bevilacqua, V., Mastronardi, G., Piscopo, G.: Evolutionary approach to inverse planning in coplanar radiotherapy. Image and vision computing 25, 196–203 (2007)
Bevilacqua, V., Aulenta, A., Carioggia, E., Mastronardi, G., Menolascina, F., Simeone, G., Paradiso, A., Scarpa, A., Taurino, D.: Metallic Artifacts Removal in Breast CT Images for Treatment Planning in Radiotherapy by Means of Supervised and Unsupervised Neural Network Algorithms. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS, vol. 4681, pp. 1355–1363. Springer, Heidelberg (2007)
Bankman, I.N.: Handbook of Medical Imaging: Processing and analysis. Academic Press, London (2000)
Bevilacqua, V., Mastronardi, G., Piazzolla, A.: An Evolutionary Method for Model-Based Automatic Segmentation of Lower Abdomen CT Images for Radiotherapy Planning. In: Di Chio, C. (ed.) EvoApplications 2010, Part I. LNCS, vol. 6024, pp. 320–327. Springer, Heidelberg (2010)
Bondiau, P.Y., Malandain, G., Chanalet, S., Marcy, P.Y., Habrand, J.L., Fauchon, F., Paquis, P., Courdi, A., Commowick, O., Rutten, I., Ayache, N.: Atlas-based Automatic Segmentation of MR Images: Validation Study on the Brainstem in Radiotherapy Context – Int J Radiat. Int. J. Radiat. Oncol. Biol. Phys. 61(1), 289–298 (2005)
Commowick, O., Arsigny, V., Isambert, A., Costa, J., Dhermain, F., Bidault, F., Bondiau, P.Y., Ayache, N., Malandain, G.: An efficient Locally Affine Framework for the Smooth Registration of Anatomical Structures. Medical Image Analysis 12(4), 427–441 (2008)
Gorthi, S., Duay, V., Bach Cuadra, M., Tercier, P.A., Allal, A.S., Thiran, J.P.: Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection. In: MICCAI Workshop on 3D Segmentation Challenge for Clinical Applications (2009)
Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)
Kass, M., Witkin, A.P., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1988)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing: Analysis and Machine Vision, 3rd edn., Thomson (2007)
Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block Matching: a General Framework to Improve Robustness of Rigid Registration of Medical Images. In: MICCAI 2000, pp. 557–566 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bevilacqua, V., Piazzolla, A., Stofella, P. (2010). Atlas-Based Segmentation of Organs at Risk in Radiotherapy in Head MRIs by Means of a Novel Active Contour Framework. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-14932-0_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14931-3
Online ISBN: 978-3-642-14932-0
eBook Packages: Computer ScienceComputer Science (R0)