Bildverarbeitung für die Medizin 2009: Algorithmen - Systeme by Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels,

By Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels, Thomas Tolxdorff

In den letzten Jahren hat sich der Workshop „Bildverarbeitung für die Medizin“ durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2009 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige in englischer Sprache - behandeln alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung, CAD, Segmentierung, Bildanalyse, Visualisierung und Animation, Roboter und Manipulatoren, Chirurgische Simulatoren, Diagnose, Therapieplanung sowie deren klinische Anwendungen.

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Corresponding CISS (left), TOF (middle) and segmented CISS (right) slices Analysis of nerve vessel contacts 23 Fig. 2. Error-bounded smoothing. From left to right: original polyline; close-up of the constrained smoothing step; smoothed polyline. 3 mm in our implementation) around the initial vertex positions p0j as depicted in figure 2: During each smoothing iteration, we compute the position pij satisfying the second order umbrella equation for each vertex pj . If pij lies outside the -ball of p0j , the vertex is projected back onto the sphere along the line from p0j to pij .

3a) and put all zero-distance voxels into a queue (active front ). For each voxel in active front we store the closest zero-distance voxel. In the next step, we propagate the distance information of each voxel v in active front to its 27 direct neighbors ni : If the current distance value D(ni ) of a neighbor voxel ni is larger than the distance d between ni and the nearest zero-distance voxel of v, D(ni ) is set to d and ni is added into the active front. After all neighbors of v have been processed, v is removed from the active front queue.

Zusammen mit der in [3] u ¨ ber eine gewichtete Mahalanobis-Distanz definier¨ ten Ahnlichkeit wird die angepasste Kantengewichtung f¨ ur nichtnegative Kanten 28 Fischer et al. ¨ ¨ aus der lokalen Ahnlichkeit si,k , der relationalen Ahnlichkeit sij,kl der beiden Hypothesen sowie einem Benefitwert ben gebildet: si,k = σV ( sij,kl = σE ( G (xi ), H (yk )) G (xi , xj ), H (yk , yl )) + σE ( (1) G (xj , xi ), H (yl , yk )) (2) Zum einen lassen sich auf diese Weise die Knotengewichtungen in Kantengewichtungen u uhren.

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