@inproceedings{EibenKunzPietrzyketal., author = {Eiben, Bj{\"o}rn and Kunz, Dietmar and Pietrzyk, Uwe and Palm, Christoph}, title = {Level-Set-Segmentierung von Rattenhirn MRTs}, series = {Bildverarbeitung f{\"u}r die Medizin 2009; Algorithmen - Systeme - Anwendungen ; Proceedings des Workshops vom 22. bis 25. M{\"a}rz 2009 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2009; Algorithmen - Systeme - Anwendungen ; Proceedings des Workshops vom 22. bis 25. M{\"a}rz 2009 in Heidelberg}, publisher = {Springer}, address = {Berlin}, pages = {167 -- 171}, abstract = {In dieser Arbeit wird die Segmentierung von Gehirngewebe aus Kopfaufnahmen von Ratten mittels Level-Set-Methoden vorgeschlagen. Dazu wird ein zweidimensionaler, kontrastbasierter Ansatz zu einem dreidimensionalen, lokal an die Bildintensit{\"a}t adaptierten Segmentierer erweitert. Es wird gezeigt, dass mit diesem echten 3D-Ansatz die lokalen Bildstrukturen besser ber{\"u}cksichtigt werden k{\"o}nnen. Insbesondere Magnet-Resonanz-Tomographien (MRTs) mit globalen Helligkeitsgradienten, beispielsweise bedingt durch Oberfl{\"a}chenspulen, k{\"o}nnen auf diese Weise zuverl{\"a}ssiger und ohne weitere Vorverarbeitungsschritte segmentiert werden. Die Leistungsf{\"a}higkeit des Algorithmus wird experimentell an Hand dreier Rattenhirn-MRTs demonstriert.}, subject = {Dreidimensionale Bildverarbeitung}, language = {de} } @inproceedings{EibenPalmPietrzyketal., author = {Eiben, Bj{\"o}rn and Palm, Christoph and Pietrzyk, Uwe and Davatzikos, Christos and Amunts, Katrin}, title = {Error Correction using Registration for Blockface Volume Reconstruction of Serial Histological Sections of the Human Brain}, series = {Bildverarbeitung f{\"u}r die Medizin 2010; Algorithmen - Systeme - Anwendungen ; Proceedings des Workshops vom 22. bis 25. M{\"a}rz 2009 in Heidelberg}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2010; Algorithmen - Systeme - Anwendungen ; Proceedings des Workshops vom 22. bis 25. M{\"a}rz 2009 in Heidelberg}, publisher = {Springer}, address = {Berlin}, pages = {301 -- 305}, abstract = {For accurate registration of histological sections blockface images are frequently used as three dimensional reference. However, due to the use of endocentric lenses the images suffer from perspective errors such as scaling and seemingly relative movement of planes which are located in different distances parallel to the imaging sensor. The suggested correction of those errors is based on the estimation of scaling factors derived from image registration of regions characterized by differing distances to the point of view in neighboring sections. The correction allows the generation of a consistent three dimensional blockface volume.}, subject = {Histologie}, language = {en} } @article{KolevKirchgessnerHoubenetal., author = {Kolev, Kalin and Kirchgeßner, Norbert and Houben, Sebastian and Csisz{\´a}r, Agnes and Rubner, Wolfgang and Palm, Christoph and Eiben, Bj{\"o}rn and Merkel, Rudolf and Cremers, Daniel}, title = {A variational approach to vesicle membrane reconstruction from fluorescence imaging}, series = {Pattern Recognition}, volume = {44}, journal = {Pattern Recognition}, number = {12}, publisher = {Elsevier}, doi = {10.1016/j.patcog.2011.04.019}, pages = {2944 -- 2958}, abstract = {Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks.}, subject = {Dreidimensionale Bildverarbeitung}, language = {en} }