УДОСКОНАЛЕННЯ РЕНТГЕНІВСЬКОЇ КОМП'ЮТЕРНОЇ ТОМОГРАФІЇ

Рентгенівська комп'ютерна томографія (РКТ) нині є найбільш перспективним і найбільш інформативним методом діагностики. За допомогою апаратури томографії можна отримати знімки безлічі перерізів тіла пацієнта, які характеризують особливості його анатомії і фізіології

A Rule-Based Expert System for Automatic Segmentation of Cerebral MRI Images

The interior boundary of medical image is fuzzy in nature. In this paper, proposed is a novel method to segment and classify the MR image of head by fuzzy clustering and fuzzy reasoning. Traditional fuzzy clustering methods are basically statistical ones in which only intensity affinities of the image are reflected. Considering the characteristics of MR image, we constructed a set of knowledge-based rules to set the fuzzy memberships of the pixels of the image by generally using the intensity similarities, positional relationships among multiple spectra MR images, and the shape features of the brain tissues and the mathematics morphological analogy of the brain tissues. Then a coarse-to-fine reasoning method is used to combine the fuzzy memberships of the pixels of the T1- and T2- channels of the image to segment the cerebral tissues into gray matter, white matter, and CSF. Experimental results showed the efficiency of the method

Mechanical based rigid registration of 3D objects: application to multimodal medical images

The registration of 3-D objects is an important problem in computer vision and especially in medical imaging. It arises when data acquired by different sensors and/or at different times have to be fused. Under the basic assumption that the objects to be registered are rigid, the problem is to recover the six parameters of a rigid transformation. If landmarks or common characteristics are not available, the problem has to be solved by an iterative method . However such methods are inevitably attracted to local minima. This paper presents a novel iterative method designed for the rigid registration of 3-D objects . Its originality lies in its physical basis : instead of minimizing an energy function with respect to the parameters of the rigid transformation (the classical approach) the minimization is achieved by studying the motion of a rigid object in a potential field. In particular we consider the kinetic energy of the solid during the registration process, which allows it to "jump over" some local maxima of the potential energy and so avoid some local minima of that energy. We present extensive experimental results on real 3-D medical images. In that particular application, we perform the matching process with the whole segmented volumes .La mise en correspondance d'objets 3D est un problème important dans le domaine du traitement d'image. Il apparaît lorsque des données acquises par différents capteurs, à des moments ou/et des instants différents doivent être fusionnées. Si l'on suppose que les objets à mettre en correspondance sont rigides, nous avons a retrouver les paramètres d'une transformation rigide. Lorsque l'utilisatin d'amers ou de caractéristiques communes n'est pas possible pour résoudre cette tache, une méthode itérative peut êre utilisée avec profit. Cet article présente une méthode itérative générale pour la mise en correspondance d'objets 3D. Son originalité réside dans ses fondements mecaniques: plutôt que de minimiser une énergie potentielle par rapport aux paramètres de la transformation rigide, qui est l'approche classique, nous étudions le mouvement d'un objet rigide, c'est-à-dire un solide, dans un champ de potentiel. Cette approche particulière prend en compte l'énergie cinétique du solide, ce qui permet de «sauter» certains maxima locaux de l'énergie potentielle et donc d'en éviter certains minima locaux. Nous montrons que notre approche, si l'on considère l'énergie cinétique toujours nulle, est équivalente à une méthode de descente de gradient, l'introduction de la vitesse permet donc d'en accélérer la convergence. En outre, nous montrons que notre méthode se laisse moins facilement «piéger» par les minima locaux de l'énergie que les méthodes classiques de minimisation. L'article est illustré par l'application de la méthode au recalage d'images médicales réelles, ou nous utilisons la totalité du volume segment

Segmentierung des Knochens aus T1- und PD-gewichteten Kernspinbildern vom Kopf

Für viele Anwendung, beispielsweise bei der Simulation biomechanischer Eigenschaften des Kopfes oder bei der Lokalisation von Hirnaktivität aus EEG/MEG-Daten, werden genaue, individuelle Modelle des Kopfes benötigt. Bislang erfolgt die Erstellung aus einem T1-gewichteten Kernspinbild. Auf diesen Bildern ist allerdings die innere Kante des Knochens nicht zu erkennen. Daher wird diese geschätzt. Das Ergebnis der genannten Anwendungen hängt aber wesentlich von der Gestalt des Knochens ab. Daher soll zukünftig ein weiteres, PD-gewichtetes Kernspinbild zur Unterstützung der Segmentierung hinzugezogen werden. In dieser Arbeit werden Algorithmen untersucht und daraus Verfahren entwickelt, um den Knochen unter Verwendung eines dual-echo-Datensatzes, bestehend aus einem T1- und einem PD-gewichteten Kernspinbild, zu segmentieren

Segmentation Of Brain Structures By Watershed Transform On Tensorial Morphological Gradient Of Diffusion Tensor Imaging

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Watershed transform on tensorial morphological gradient (TMG) is a new approach to segment diffusion tensor images (DTI). Since the TMG is able to express the tensorial dissimilarities in a single scalar image, the segmentation problem of DTI is then reduced to a scalar image segmentation problem. Therefore, it can be addressed by well-known segmentation techniques, such as the watershed transform. In other words, by computing the TMG of a DTI, and then using the hierarchical watershed transform, it is possible to segment brain structures, such as the corpus callosum, the ventricles and the cortico-spinal tracts, and use the results for subsequent quantitative analysis of DTI parameters. Experiments showed that segmentations obtained with the proposed approach are similar to the ones obtained by other segmentation techniques based on DTI and also segmentation methods based on other Magnetic Resonance Imaging (MRI) modalities. Since the proposed method, as opposed to the majority of the DTI based segmentation methods, does not require manual seed and/or surface placement, its results are highly repeatable. 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Grenander, Mathematical textbook of deformable neuroanatomies, Proceedings of the National Academy of Sciences of the United States of America, 90, no. 24, pp. 11 944-11 948, 1993Collins, L.D., Holmes, C.J., Peters, T.M., Evans, A.C., Automatic 3-D model-based neuroanatomical segmentation (1995) Human Brain Mapping, 3 (3), pp. 190-208Collins, D.L., Zijdenbos, A.P., Baaré, W.F.C., Evans, A.C., ANIMAL+INSECT: Improved cortical structure segmentation (1999) IPMI 99: Proceedings of the 16th International Conference on Information Processing in Medical Imaging, pp. 210-223. , London, UK: Springer-VerlagZhukov, L., Museth, K., Breen, D., Whitaker, R., Barr, A., Level set modeling and segmentation of DT-MRI brain data (2003) Journal of Electronic Imaging, 12, pp. 125-133Wang, Z., Vemuri, B., DTI segmentation using an information theoretic tensor dissimilarity measure (2005) IEEE Transactions on Medical ImagingJonasson, L., Bresson, X., Hagmann, P., Cuisenaire, O., Meuli, R., Thiran, J., White matter fiber tract segmentation in DT-MRI using geometric flows (2005) Medical Image Analysis, 9 (3), pp. 223-236Weldeselassie, Y., Hamarneh, G., DT-MRI segmentation using graph cuts (2007) Medical Imaging 2007: Image Processing, Proceedings of the SPIE, , SPIEY. 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Jolly, Interactive graph cuts for optimal boundary and region segmentation of objects in ND images, International Conference in Computer Vision, 01, p. 105, 2001Lenglet, C., Rousson, M., Deriche, R., A statistical framework for DTI segmentation (2006) Proceedings of the International Symposium on Biomedical Imaging, pp. 794-797. , IEEENiogi, S.N., Mukherjee, P., McCandliss, B.D., Diffusion tensor imaging segmentation of white matter structures using a reproducible objective quantification scheme (ROQS) (2007) NeuroImage, 35, pp. 166-174Rittner, L., Flores, F., Lotufo, R., New tensorial representation of color images: Tensorial morphological gradient applied to color image segmentation (2007) SIBGRAPI07: Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing, pp. 45-52. , Belo Horizonte, Brazil: IEEE Computer SocietyRittner, L., Lotufo, R., Diffusion tensor imaging segmentation by watershed transform on tensorial morphological gradient (2008) SIBGRAPI08: Proceedings of the XXI Brazilian Symposium on Computer Graphics and Image Processing, pp. 196-203. , Campo Grande, Brazil: IEEE Computer SocietyHeijmans, H.J.A.M., (1994) Morphological Image Operators, , Boston: Academic PressPierpaoli, C., Basser, P.J., Toward a quantitative assessment of diffusion anisotropy (1996) Magnetic Resonance in Medicine, 36 (6), pp. 893-906Alexander, D., Gee, J., Bajcsy, R., Similarity measures for matching diffusion tensor images Proceedings of the British Machine Vision Conference, 1, pp. 93-102. , Nottingham, UK, ppJones, A.S.D.K., Horsfield, M.A., Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging (1999) Magnetic Resonance in Medicine, 42 (3), pp. 515-525Wiegell, M., Tuch, D., Larson, H., Wedeen, V., Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging (2003) NeuroImage, 19, pp. 391-402Ziyan, U., Tuch, D., Westin, C., Segmentation of thalamic nuclei from DTI using spectral clustering (2006) ser. 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