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%.
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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
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DOI: https://doi.org/10.1007/10983652_26
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