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Image Restoration Using Functional and Anatomical Information Fusion with Application to SPECT-MRI Images

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      Abstract

      Image restoration is usually viewed as an ill-posed problem in image processing, since there is no unique solution associated with it. The quality of restored image closely depends on the constraints imposed of the characteristics of the solution. In this paper, we propose an original extension of the NAS-RIF restoration technique by using information fusion as prior information with application in SPECT medical imaging. That extension allows the restoration process to be constrained by efficiently incorporating, within the NAS-RIF method, a regularization term which stabilizes the inverse solution. Our restoration method is constrained by anatomical information extracted from a high resolution anatomical procedure such as magnetic resonance imaging (MRI). This structural anatomy-based regularization term uses the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MRI and SPECT data volumes from each patient. This method was successfully tested on 30 pairs of brain MRI and SPECT acquisitions from different subjects and on Hoffman and Jaszczak SPECT phantoms. The experiments demonstrated that the method performs better, in terms of signal-to-noise ratio, than a classical supervised restoration approach using a Metz filter.

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      Most cited references 33

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          A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized. In our derivation of the registration procedure, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with image data; no pre-processing or segmentation is required. This technique is, however, more flexible and robust than other intensity-based techniques like correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with computed tomography (CT) images, and with positron-emission tomography (PET) images. Surgical applications of the registration method are described.
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            Author and article information

            Affiliations
            1Department of Computer Science and Operations Research (DIRO), University of Montreal, CP 6128l, Station Centre-Ville, P.O. Box 6128, Montréal, QC, Canada H3C 3J7
            2McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montréal, QC, Canada H3A 2B4
            Author notes

            Recommended by Jun Zhao

            Journal
            Int J Biomed Imaging
            IJBI
            International Journal of Biomedical Imaging
            Hindawi Publishing Corporation
            1687-4188
            1687-4196
            2009
            1 October 2009
            : 2009
            2756467
            19812704
            10.1155/2009/843160
            Copyright © 2009 S. Benameur et al.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Research Article

            Radiology & Imaging

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