Data fusion in medical imaging: merging multimodal and multipatient images, identification of structures and 3D...

Data fusion in medical imaging can be seen into two ways (i) multisensors fusion of anatomical and functional information and (ii) interpatient data fusion by means of warping models. These two aspects set the methodological framework necessary to perform anatomical modelling especially when concerning the modelling of brain structures. The major relevance of the work presented here concerns the interpretation of multimodal 3D neuro-anatomical data bases. Three types of data fusion problems are considered in this paper. The first one concerns the problem of data combination which includes multimodal registration (multisensor fusion applied to CT, MRI, DSA, PET, SPECT, or MEG). In particular, the problem of warping patient data to an anatomical atlas is reviewed and a solution is proposed. The second problem of data fusion addressed in this paper is the identification of anatomical structures by means of image analysis methods. Two techniques have been developed. The first one deals with the analysis of image geometrical features to end up with the determination of a fuzzy mask to label the structure of interest. The second technique consists of labelling major cerebral structures by means of statistical image features associated with relaxation techniques. Finally, the paper presents a review of up to date 3D display techniques with a special emphasis on volume rendering and 3D display of combined data.