Bildverarbeitung für die Medizin 2006 Algorithmen Systeme by Heinz Handels, Jan Ehrhardt, Alexander Horsch, Hans-Peter

By Heinz Handels, Jan Ehrhardt, Alexander Horsch, Hans-Peter Meinzer, Thomas Tolxdoff

In den letzten Jahren hat sich der Workshop "Bildverarbeitung f?r die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2006 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespr?che zwischen Wissenschaftlern, Industrie und Anwendern. Die Beitr?ge dieses Bandes - einige in englischer Sprache - behandeln alle Bereiche der medizinischen Bildverarbeitung, insbesondere Algorithmen, tender- und Hardwaresysteme sowie deren klinische Anwendungen.

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H. Menze et al. Results Areas with high signal-to-noise ratio were reliably identified, virtually all artifacts being present in the data set were separated. 04 % is most probably due to ambivalent training labels. The classifier proposed to reject 60 low quality spectra (16/10/34) from the data set for the tumor classification. Within that data set, the randomForest Gini importance identified the spectral regions of the metabolites choline and NAA to be relevant for the classification. Thus a subset of Psubset = 14 out of the 256 spectral channels was used for training and evaluation of the binomial PLS model (fig.

The signal is modeled to be approximately homogeneous in a small neighborhood. Furthermore, in practice, the spatial filter is a (linear) binomial filter h of small size (3 × 3 to 7 × 7 pixels). Assume the neighborhood of a pixel to be corrupted with noise of standard deviation σ0 . Then the spatio-temporal filtered image exhibits noise of strength σst (t) = β 2t + (β 2t − 1) a2 2 α + a1 α + (β − 1) · σ0 β−1 (4) with: a1 = 2(1 − h0,0 ) K (5) K [1 − δ(i)δ(j)] h2i,j + (1 − h0,0 )2 a2 = (6) i=−K j=−K For static regions (t → ∞) and constant β < 1, the optimum parameters are given by a1 α= (1 − β) (7) 2a2 and γ = 1 − (α + β).

Dazu sollen die auftretenden Kr¨ afte und Belastungen des Gelenkes vor und nach dem Eingriff mit Hilfe validierter biomechanischer Modelle berechnet werden, um eine m¨ oglichst optimale biomechanische Rekonstruktion des H¨ uftgelenkes f¨ ur jeden Patienten gew¨ ahrleisten zu k¨ onnen. Die Grundlage f¨ ur die Operationsplanung sind in der Regel R¨ontgenbilder. W¨ahrend aus einer Becken¨ ubersichtsaufnahme (koronale Projektion) bereits wesentliche Daten f¨ ur eine Anpassung biomechanischer Modelldaten zur Berechnung der Belastungsbedingungen der H¨ ufte gewonnen werden k¨onnen [1], erfordert eine genauere Belastungsanalyse die Kenntnis der 3D-Geometrie der Anatomie, insbesondere der Knochen und Muskeln.

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