Bildverarbeitung für die Medizin 2014: Algorithmen - Systeme by Thomas Martin Deserno, Heinz Handels, Hans-Peter Meinzer,

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

In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2014 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Molekulare Bildgebung, Visualisierung und Animation, Bildsegmentierung und -fusion, Anatomische Atlanten, Zeitreihenanalysen, Biomechanische Modellierung, Klinische Anwendung computerunterstützter Systeme, Validierung und Qualitätssicherung u.v.m.

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Extra resources for Bildverarbeitung für die Medizin 2014: Algorithmen - Systeme - Anwendungen Proceedings des Workshops vom 16. bis 18. März 2014 in Aachen

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The Google Maps API, in combination with a JavaScript framework, turned out to be very useful for rapid application development. It is web-based and offers a user experience with fast and smooth transitions of images, which has been proven by millions of users. In addition to its core functionality – the intuitive switching from one stain or classification result view to another, while keeping corresponding regions in the center of the main view (Fig. 1 a) – the user interface was extended with the following views and functions: – Multiview: views of corresponding regions of multiple serial sections stained with multiple markers, by means of a panel with several (small) viewports (Fig.

Parameters φ are estimated using the Expectation-Maximization algorithm with all n voxels: n L(φ) = i=1 p(xi , φ). ,c} classification of that pulmonary region. After removing trachea and bronchi, left and right lungs are separated. 2. A Gibbs-Markov random field describes the prior probability of the Markov random field X that contains the class xk ∈ {1, . . , c} in each CT slice with BB , where Z, T are constant, nA is the number P (X) = Z1 exp − nA A+n T of horizontal and vertical, and nB of diagonal √ inhomogeneous second order cliques.

2 c). – Smart cursor: provide quantitative data from image analysis to the stain region currently indicated by the mouse cursor. (a) (b) (c) Fig. 2. (a) Heat map representation of a quantitative image analysis result as colored overlay to an overview of the virtual slides. As an example the spatial distribution of the expression of a progesterone receptor staining is shown. (b) Detailed view from a section of this staining where some sample points of interest are marked by flag symbols. Different colors of the flags indicate on which stain they have been set.

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