Download Bildverarbeitung für die Medizin 2008: Algorithmen - Systeme by Thomas Tolxdorff, Jürgen Braun, Thomas Martin Deserno, Heinz PDF

By Thomas Tolxdorff, Jürgen Braun, Thomas Martin Deserno, Heinz Handels, Alexander Horsch, Hans-Peter Meinzer

In den letzten Jahren hat sich der Workshop „Bildverarbeitung für die Medizin“ durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2008 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, Computerunterstützte Diagnose, Therapieplanung sowie deren klinische Anwendungen.

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For comparison, two polynomial algorithms were used. In the sinogram we achieved an improvement of the image quality using the NFFT interpolation. However, in the reconstructed images we obtained only a marginal improvement compared to spline based interpolation. This behavior may be due to the smoothness of the NFFT interpolation, since the filtered backprojection includes a high pass filtering. Taking this into account, an appropriate choice of the damping factors w ˆ may yield better reconstruction results.

Scene with texture a (left), with texture b (middle) and, with no texture (right). For the textures see Fig. 2 32 C. Schaller et al. intuitive handling on a common PC by combining polygonal surface rendering with a guided-navigation paradigm. In virtual endoscopy, crucial depth information is significantly reduced by projecting a 3D scene onto a 2D screen. One of the main ideas of illustrative rendering is to provide additional visual cues for the interpretation of a scene. The most important source of depth cues in virtual endoscopy is the relative movement of objects due to shifts in perspective (motion parallax).

The DCE T1 -weighted features are circularity and variance of the radial gradient histogram as described in [5], reflecting the lesion shape and the sharpness of its margin, respectively. 6 1−Specificity Fig. 1. 8 z 1 Computer-Aided Diagnosis in Breast MRI 15 three T2 -weighted features are the normalized α-percentile for the eroded lesion segmentation (α = 20%) and the normalized α-percentile for the lesion shell with (α = 92%, t = 2mm) and (α = 98%, t = 20mm). The latter features resemble clinical practice of rating the intensities within the lesion and within the enclosing shell of non-lesion tissue.

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