19 research outputs found

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

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    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

    AN EDGE-PRESERVING ANATOMICAL-BASED REGULARIZATION TERM FOR THE NAS-RIF RESTORATION OF SPECT IMAGES

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    Nowadays, brain 3D SPECT is a well established functional imaging method that is widely used in clinical settings for the assessment of neurological and cerebrovascular diseases. However, due to the scattering of the emitted photons, inherent to this imaging process, brain 3D SPECT images exhibit poor spatial and inter-slice resolution. More precisely, SPECT images are blurred, leading to substantial errors in measurement of regional brain activity and making difficult and subjective, a reliable and accurate diagnosis by the nuclear physician. In order to improve the resolution of these images and then to facilitate their interpretation, we herein propose an original extension of the NAS-RIF deconvolution technique of Kundur and Hatzinakos [1]. The proposed extension has two interesting properties; it allows to exploit or fuse anatomical and geometrical information extracted from a high resolution anatomical magnetic resonance (MR) image and also to efficiently incorporate, into the NAS-RIF method, a regularization term to stabilize the inverse solution. In our application, this anatomical-based regularization term exploits the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MR and SPECT data volume coming from a same patient. This method has been successfully tested on numerous pairs of brain MR and SPECT images of different patients, yielding very promising restoration results. Index Terms — 3D blind deconvolution, unsupervised segmentation, 3D/3D registration, image restoration, SPECT imagery. 1

    Dictionary of Invertebrate Zoology

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    An exhaustive dictionary of over 13,000 terms relating to invertebrate zoology, including etymologies, word derivations and taxonomic classification. Entries cover parasitology, nematology, marine invertebrates, insects, and anatomy, biology, and reproductive processes for the following phyla: Acanthocephala Annelida Arthropoda Brachiopoda Bryozoa Chaetognatha Cnidaria Ctenophora Echinodermata Echiura Entoprocta Gastrotricha Gnathostomulida Kinorhyncha Loricifera Mesozoa Mollusca Nemata Nematomorpha Nemertea Onychophora Pentastoma Phoronida Placozoa Platyhelminthes Pogonophora Porifera Priapula Rotifera Sipuncula Tardigrada.https://digitalcommons.unl.edu/zeabook/1061/thumbnail.jp

    The Adaptive City

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