Abstract
In imaging, physical phenomena and acquisition system often induce an alteration of the information. It results in the presence of noise and partial volume effect corresponding respectively to uncertainties and imprecisions. To cope with these different imperfections, we propose a method based on information fusion using Belief function theory. First, it takes advantage of neighborhood information and combination rules on mono-modal images in order to reduce uncertainties due to noise while considering imprecisions due to partial volume effect on disjunctions. Imprecisions are then reduced using information coming from multi-modal images. Results obtained on simulated images using various signal to noise ratio and medical images show its ability to segment multi-modal images having both noise and partial volume effect.
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
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat.Ā 38, 225ā339 (1967)
Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)
Smets, P., Kennes, R.: The Transferable Belief Model. Artif. Intell.Ā 66, 191ā234 (1994)
Capelle, A.S., Colot, O., Fernandez-Maloigne, C.: Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information. Inf. FusionĀ 5(3), 203ā216 (2004)
Zhang, P., Gardin, I., Vannoorenberghe, P.: Information fusion using evidence theory for segmentation of medical images. In: Int. Colloq. on Inf. Fusion, vol.Ā 1, pp. 265ā272 (2007)
Bloch, I.: Defining belief functions using mathematical morphology - Application to image fusion under imprecision. Int. J of Approx. Reason.Ā 48(2), 437ā465 (2008)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Gould, M.K., Kuschner, W.G., Rydzak, C.E., et al.: Test performance of positron emission tomography and computed tomography for mediastinal staging in patients with non-small-cell lung cancer: a meta-analysis. Ann. Int. Med.Ā 139, 879ā892 (2003)
Xu, B., Guan, Z., Liu, C., et al.: Can multimodality imaging using 18F-FDG/18F-FLT PET/CT benefit the diagnosis and management of patients with pulmonary lesions? Eur. J. Nucl. Med. Mol. ImagingĀ 38(2), 285ā292 (2011)
Choi, W., Lee, S.W., Park, S.H., et al.: Planning study for available dose of hypoxic tumor volume using fluorine-18-labeled fluoromisonidazole positron emission tomography for treatment of the head and neck cancer. Radiother. Oncol.Ā 97(2), 176ā182 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lelandais, B., Gardin, I., Mouchard, L., Vera, P., Ruan, S. (2012). Using Belief Function Theory to Deal with Uncertainties and Imprecisions in Image Processing. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-29461-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
Online ISBN: 978-3-642-29461-7
eBook Packages: EngineeringEngineering (R0)