Abstract
A fuzzy information fusion scheme is proposed in this paper to automatically segment tumor areas of human brain from multispectral magnetic resonance images such as T1-weighted, T2-weighted and Proton Density (PD) feature images. The proposed scheme consists of four stages: data-level fusion, space creation of fuzzy features, fusion of fuzzy features and fuzzy decision. Several fuzzy operators are proposed to create the feature-level fusion. The fuzzy information models describing the characteristics of tumor areas in human brain are also established. A fuzzy region growing based on fuzzy connecting is presented to obtain the final segmentation result. The comparison between the result of our method and the hand-labeled segmentation of a radiology expert shows that this scheme is efficient. The experimental results (based on 4 patients studied) show an average probability of correct detection equal to 96% and an average probability of false detection equal to 5%.
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
Pitiot, A., Toga, A.W., Thompson, P.M.: Adaptive Elastic Segmentation of Brain MRI via Shape-Model-Guided Evolutionary Programming. IEEE Trans. on med. imag. 21, 910–923 (2002)
Fan, Y., Jiang, T., Evans, D.J.: Volumetric Segmentation of Brain Images Using Parallel Genetic Algorithms. IEEE Trans. on med. imag. 21, 904–909 (2002)
Barra, V., Boire, J.: Automatic Segmentation of Subcortical Brain Structures in MR Images Using Information Fusion. IEEE Trans. on Med. Imag. 20, 549–558 (2001)
Styner, M., Brechbühler, C., Székely, G., Gerig, G.: Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans. on Med. Imag. 19, 153–165 (2000)
Schroeter, P., Vesin, J.-M., Langenberger, T., Meuli, R.: Robust Parameter Estimation of Intensity Distributions for Brain Magnetic Resonance Images. IEEE Trans. on Med. Imag. 17, 172–186 (1998)
Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: A Unifying Framework for Partial Volume Segmentation of Brain MR Images. IEEE Trans. on Med. Imag. 22, 105–119 (2003)
Ruan, S., Moretti, B., Fadili, J., Bloyet, D.: Fuzzy Markovian Segmentation in Application of Magnetic Resonance Images. Computer Vision and Image Understanding 85, 54–69 (2002)
Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, M.S.: Automatic Tumor Segmentation Using Knowledge-Based Techniques. IEEE Trans. on Med. Imag. 17, 187–201 (1998)
Udupa, J.K., Wei, L., Samarasekera, S., Miki, Y., van Buchem, M.A., Grossman, R.I.: Multiple Sclerosis Lesion Quantification using Fuzzy-Connectedness Principles. IEEE Trans. on Med. Imag. 16, 598–609 (1997)
Clark, M.C., Hall, L.O., Goldgof, D.B., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S.: MRI Segmentation using Fuzzy Clustering Techniques. IEEE Engineering in Medicine and biology, 730–742 (November/December, 1994)
Jenkinson, M., Smith, S.: Optimization in Robust Linear Registration of Brain Images. FMRIB Technical Report TR00MJ2
Hall, D.L.: Mathematical Techniques in Multisensor Data Fusion. Artech House, Inc (1992)
Warfield, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive, Template Moderated, Spatially Varying Statistical Classification. Medical Image Analysis 4, 43–55 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dou, W., Ruan, S., Liao, Q., Bloyet, D., Constans, JM., Chen, Y. (2006). Fuzzy Information Fusion Scheme Used to Segment Brain Tumor from MR Images. In: Di Gesú, V., Masulli, F., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2003. Lecture Notes in Computer Science(), vol 2955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10983652_26
Download citation
DOI: https://doi.org/10.1007/10983652_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31019-8
Online ISBN: 978-3-540-32683-0
eBook Packages: Computer ScienceComputer Science (R0)