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1.1 A definition of information and image fusion The principle of information fusion is widely used in image-based processes where the data are acquired from several sources, e.g. in the fields of remote sensing (Lennon et al., 2000), satellite (Wald, 2002) or biomedical imaging (Barra & Boire, 2001a). L. Wald defined the fusion operation as “a formal framework in which are expressed the means and tools for the alliance of data originating from different sources”. According to the same author, the aim of a fusion process is to improve the quality of the available information, where the notion of quality depends on the application context. I. Bloch et al. give a more specific definition of fusion: “data fusion consists in combining several pieces of information issued from different sources about the same phenomenon, in order to take a better decision on this phenomenon” (Bloch & Maitre, 1997). In accordance with this statement, the fusion of image data is viewed as the joined use of heterogeneous images for decision aid. The first and most obvious difference between these definitions (and the numerous others proposed in the literature) lies in the aim of the fusion process. That is, the goal to reach is here either to improve the information provided by the different sources, each of them considered as being imperfect (Dubois & Prade, 1994), or to take a decision about the observed scene. The first objective may be viewed as a qualitative improvement (reducing information imperfections), while in the second case the aim is to reduce the doubt about the validity of the decision by increasing the amount of available information. This last case may thus also be viewed as a quantitative improvement of the information. Whatever the definition for the fusion process, data stemming from one source are generally used to compensate for a lack of information or as a medium for complementary features about the physical object or phenomenon studied. In this chapter, image fusion will refer to a computer-based process aiming at extracting knowledge from an image set, which was obviously not visible in the original images. This new information may consist in either image data (visual result) (Aguilar & New, 2002; Montagner et al., 2005a), numerical indexes (Wang et al., 1998; Montagner et al., 2005b), or even subsets of image regions (image segmentation in e.g. binary sets) (Bloch et al., 2003). O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Multilevel Information Fusion: A Mixed Fuzzy Logic/Geometrical Approach with Applications in Brain Image Processing