@ARTICLE{Gehring2009,
  author = {H. Gehring and L. Meyer zu Westrup and S. Boye and A. Opp and U. Hofmann},
  title = {Transcranial doppler, EEG and SEP monitoring},
  journal = {Applied Cardiopulmonary Pathophysiology},
  year = {2009},
  volume = {13},
  pages = {224-236},
  number = {3}
}

 @ARTICLE{Loeffler2008, 
  author = {L{\"o}ffler, S. and Vogt, S. and Hofmann, U. G. and Moser, A.},
   title = {Striatal microstimulation in awake animals depends on NMDA receptor activity},
   year = {2008},  
   journal = {Biomed. Technik / Biomedical Engineering},
   volume = {53},
   number = {Supp 1},
   pages = {241-243}
   
}

@ARTICLE{Maciak2008,
  author = {Maciak, A. and Seidel, G. and Meyer-Wiethe, K. and Kier, C. and Hofmann, U.G.},
  title = {Bolus Harmonic Imaging zur automatischen Erkennung ischämiebedingter
	Perfusionsdefizite im Gehirn},
  journal = {Ultraschall in der Medizin},
  year = {2008},
  volume = {29},
  pages = {--},
  abstract = {Zusammenfassung
	
	
	Ziel: Die Diagnose zerebrovaskulärer Erkrankungen stützt sich zunehmend
	auf den Einsatz ultraschallbasierter Verfahren. Das kontrastmittelgestützte,
	transkranielle Bolus Harmonic Imaging (BHI) hat hierbei einen hohen
	Stellenwert. Die Auswertung der aufgezeichneten Bildsequenz erfolgt
	durch geübte Ärzte jedoch manuell und ist somit zeitaufwendig. Das
	Ziel der hier beschriebenen drei Verfahren ist die sichere, vollautomatische
	Erkennung von Perfusionsdefekten des Gehirns. Material und Methoden:
	Mit dem BHI werden Ultraschallbildsequenzen aufgezeichnet, die die
	Kontrastmitteldynamik im Gehirn wiedergeben. Diese Bilder werden
	mit drei verschiedenen Verfahren automatisch ausgewertet. Zum einen
	wird ein regelbasiertes System beschrieben, welches aus den aufgezeichneten
	Ultraschallbildern Parameterbilder extrahiert und diese mithilfe
	von Expertenwissen nach perfundierten und minderperfundierten Gehirnarealen
	klassifiziert. Zum zweiten erfolgt die Klassifikation der Gehirngebiete
	unüberwacht mit dem K-Means-Verfahren. Hierzu wird jedes der Parameterbilder
	als eine Dimension des zu klassifizierenden Merkmalsraums betrachtet,
	sodass eine unüberwachte Segmentierung der minderperfundierten Gehirnbereiche
	möglich ist. Drittens wird die gesamte Bildsequenz anhand der Kontrastmitteldynamik
	pixelweise klassifiziert. Hierbei kann darauf verzichtet werden,
	Parameterbilder extrahieren zu müssen. In allen drei Fällen ist es
	im Anschluss notwendig, die beim HI auftretenden Streifenartefakte
	automatisch zu erkennen. Abschließend wird ein Aussagenbild generiert,
	in dem die gefundenen Minderperfusionen markiert sind. Ergebnisse:
	Die drei Verfahren wurden auf einem 26 Patienten umfassenden Kollektiv
	klinisch validiert. Hierbei hat sich herausgestellt, dass insbesondere
	die Segmentierung anhand der Kontrastmitteldynamik dazu geeignet
	ist, Minderperfusionen automatisch zu erkennen. Es konnte auf diesem
	Patientenkollektiv eine Sensitivität von 100 % bei einer Spezifität
	von 100 % erreicht werden. Schlussfolgerungen: Alle drei Verfahren
	erscheinen geeignet, ischämische Gehirngebiete zu erkennen. Hierbei
	liefert die Klassifikation von Gehirngebieten nach der Kontrastmitteldynamik
	die besten Ergebnisse, da sie robust gegenüber Rauschen ist. Zudem
	ist es das schnellste Verfahren, da die Extraktion von Parameterbildern
	entfällt. Es ist notwendig, die Sensitivität und Spezifität auf einem
	größeren Patientenkollektiv zu validieren. Eine sichere, vollautomatische
	Erkennung von Perfusionsdefekten direkt am Patientenbett scheint
	somit möglich zu sein.
	
	Abstract
	
	
	Purpose: The diagnosis of ischemic stroke relies increasingly on the
	usage of ultrasound-based methods. One of the recent methods is the
	transcranial, contrast agent-based Bolus Harmonic Imaging (BHI) method.
	The captured image sequence is manually examined by clinical experts
	thus resulting in a time-consuming procedure. The purpose of this
	study is to evaluate three different methods to analyze BHI image
	sequences automatically for the detection of ischemic brain tissue.
	Materials and Method: BHI captures an image sequence that provides
	information on the dynamic behavior of the ultrasound contrast agents.
	This image sequence is analyzed using three different procedures.
	First a system relying on expert knowledge is used to determine perfusion
	defects. This procedure requires parametric images, which are previously
	extracted from the image sequence. The parameter images are then
	categorized by an unsupervised classification method in well-perfused
	and ischemic tissue by regarding the parametric images as features
	describing the perfusion. Thirdly, the whole image sequence can be
	interpreted as a pixel-by-pixel behavior out of contrast agents.
	The dynamic curve of each pixel can be automatically classified as
	perfused and ischemic tissue by the K-Means method without extracting
	parametric images. In all three cases a closing step is necessary
	for the accurate interpretation of the results. Transcranial ultrasound
	imaging produces typical stripe artifacts that have to be detected
	and eliminated. A result image is then created and provides a conclusion
	about perfusion reduction in brain tissue. Results: All three methods
	have been validated on the basis of 26 patients by clinical experts.
	The segmentation on the contrast agent kinetics has proven to be
	most effective. According to our patient database, it provides the
	highest detection accuracy, resulting in a sensitivity of 100 % and
	a specificity of 100 %. Conclusion: The presented methods seem to
	be adequate for detecting ischemic brain tissue. The classification
	of contrast agent kinetics provides the best results and has further
	advantages. It is robust with respect to noise and the calculation
	is fast because the extraction of parametric images is omitted. The
	very high sensitivity and specificity must be validated in a larger
	patient population. Reliable and automated detection of perfusion
	defects at the bedside seems to be possible.},
  booktitle = {Automatic Detection of Perfusion Deficits with Bolus Harmonic Imaging},
  doi = {10.1055/s-2008-1027190},
  refid = {101055S20081027190}
}

@INPROCEEDINGS{Vogt2008,
author = {S. Vogt and M. Klostermann and A. Kundu and S. Andruschenko and U.G. Hofmann.},
   title = {Hardware-in-the-loop testbed for closed-loop brain stimulators},
   booktitle = {ECIFMBE 2008- IFMBE Proceedings},
   year = {2008},
   editor = {Sloten, J. Vander and Verdonck, P. and Nyssen, M. and Haueisen, J.},
   address = {Antwerp},
   publisher = {IFMBE Proceedings ISSN },
   volume = {22},
   pages = {1128-1132}
   
}

@ARTICLE{DaroltiTIP08,
 author = {Cristina Darolti and Christoph Bodensteiner and Alfred Mertins and Ulrich G. Hofmann},
 title = {Local Region Descriptors for Active Contours Evolutions},
 journal = {IEEE Transactions on Image Processing},
 volume = {17},
   number = {12},
pages={2275-2288},
 year = {2008}
} 

@masterthesis{Kier_sdl2ses_2002,
        author = {Kier, Christian},
        title = {{Automatic Transformation of Finite State Machines Specified in SDL for Performance Evaluation -- Development of sdl2ses}},
        school = {{University of Luebeck}},
        year = 2002,
        address = {Luebeck}
}

 @INPROCEEDINGS{Bleil2008,
author = {M. Bleil and R. Linder and A. Opp and S. Boye and H. Gehring and U.G. Hofmann},
   title = {Online-Classification of Capnographic Curves Using Artificial Neural Networks},
   booktitle = {ECIFMBE 2008- IFMBE Proceedings},
   editor = {Sloten, J. Vander and Verdonck, P. and Nyssen, M. and Haueisen, J.},
   address = {Antwerp},
   publisher = {IFMBE Proceedings ISSN },
   volume = {22},
   pages = {1096-1099},
   year = {2008}
}

 @ARTICLE{Loeffler2008, 
  author = {L{\"o}ffler, S. and Vogt, S. and Hofmann, U. G. and Moser, A.},
   title = {Striatal microstimulation in awake animals depends on NMDA receptor activity},
   year = {2008},  
   journal = {Biomed. Technik / Biomedical Engineering},
   volume = {53},
   number = {Supp 1},
   pages = {241-243}
   
}

 @INPROCEEDINGS{Chamadiya2008,
author = {B.K. Chamadiya and S. Heuer and U.G. Hofmann and M. Wagner},
   title = {Towards a capacitively coupled electrocardiography system for car seat integration},
   booktitle = {ECIFMBE 2008- IFMBE Proceedings},
   editor = {Sloten, J. Vander and Verdonck, P. and Nyssen, M. and Haueisen, J.},
   address = {Antwerp},
   publisher = {IFMBE Proceedings ISSN},
   volume = {22},
   pages = {1217-1221},
   year = {2008}
}

@INPROCEEDINGS{Melzer2008,
author = {A. Melzer and B.J. Bushman and U.G. Hofmann},
   title = {When Items Become Victims: Brand Memory in Violent and Nonviolent Games},
   booktitle = {ICEC 2008 - 7th International Conference on Entertainment Computing},
   address = {Pittsburgh},
   publisher = {Springer},
   volume = {Lecture Notes in Computer Science - Entertainment Computing},
   year = {2008}
}

@INPROCEEDINGS{DaroltiACIVS,
  author = {Cristina Darolti and Alfred Mertins and Ulrich G. Hofmann},
  title = {A Fast Level-Set Method for Accurate Tracking of Articulated Objectswith
	An Edge-Based Binary Speed Term},
  booktitle = {Advanced Concepts for Intelligent Vision Systems},
  year = {2007},
  editor = {Jacques Blanc-Talon, Wilfried Philips, Dan Popescu und Paul Scheunders},
  number = {4678},
  series = {Lecture Notes in Computer Science},
  pages = {828-839},
  month = {August},
  publisher = {Springer},
  owner = {darolti},
  timestamp = {2007.07.13}
}

@ARTICLE{Kier2007,
  author = {Christian Kier and Christina Cyrus and Günter Seidel and Ulrich G. Hofmann and Til Aach},
  title = {Segmenting the substantia nigra in ultrasound images for early diagnosis
	of Parkinson's disease},
  journal = {International Journal of Computer Assisted Radiology and Surgery},
  year = {2007},
  volume = {2},
  pages = {S83--S85},
  number = {S1},
  month = {June},
  doi = {10.1007/s11548-007-0086-4},
  keywords = {parkinson}
}

@INPROCEEDINGS{Maciak2007,
  author = {Maciak, A. and Kier, C. and Seidel, G. and Meyer-Wiethe, K. and Hofmann, U. G.},
  title = {Automatische Erkennung von Ischämien mit Bolus Harmonic Imaging},
  booktitle = {Bildverarbeitung für die Medizin 2007},
  year = {2007},
  editor = {A. Horsch, T. M. Deserno, H. Handels, H.-P. Meinzer, T. Tolxdorf},
  pages = {81-86},
  address = {München},
  month = {25-27 March},
  publisher = {Springer-Verlag},
  isbn = {3540710905}
}

@InProceedings{Condurache07,
	title = {Automatic measuring of quality criteria for heart valves},
	booktitle = {Medical Imaging 2007: Image Processing},
	author = {A. P. Condurache and T. Hahn and M. Scharfschwerdt and M. Misfeld and U. G. Hofmann and T. Aach},
	publisher = {SPIE},
	address = {San Diego, CA},
	month ={February},
	year = {2007},
}

@inproceedings{MankodiyaBMT07,
   Author = {Mankodiya, K.  and Ayoub, A.  and Vogt, S.  and Opp, A.  and G{\"o}nne, M.  and Linder, R.  and Gehring, H.  and Hofmann, U. G. },
   Title = {A design study on a multisensory cerebral monitor},
   BookTitle = {BMT 2007},
   Address= {Aachen},
   Publisher = {de Gruyter},
      Year = {2007} }

@inproceedings{Gritsun07,
   Author = {Gritsun, T. and G. Engler and Moll, C.K.E. and Engel, A.K. and Kondra, S. and Hofmann, L. Ramrath
Ulrich G.},
   Title = {A simple microelectrode bundle for deep brain recordings},
   BookTitle = {3rd Int'l Conference on Neural Engineering},
   Editor = {Akay, Metin},
   Address= {Hawaii, USA},
   Publisher = {IEEE},
      Year = {2007} }

@ARTICLE{Maciak2007a,
  author = {A. Maciak and C. Kier and G. Seidel and K. Meyer-Wiethe and U. G.
	Hofmann},
  title = {Detecting Stripe Artifacts in Ultrasound Images},
  journal = {Journal of Digital Imaging},
  year = {2007},
  doi = {10.1007/s10278-007-9049-0},
  url = {http://www.springerlink.com/content/rm058658xpv37ph2/?p=fbae785cac25420b93dd3c1a6b1428a9&pi=0}
}

@article{Hiller2007,
   Author = {Hiller, A. and Loeffler, S. and Haupt, Ch. and Litza, M. and Hofmann, U. G. and Moser, A.},
   Title = {Electrical High Frequency Stimulation Induces GABA Outflow in Freely Moving Rats},
   Journal = {J. Neurosci. Methods},
   Volume = {159},
   Number = {2},
   Pages = {286-290},
      Year = {2007} } 

@inproceedings{Jensen07,
   Author = {Jensen, W. and Yoshida, K. and Hofmann, U. G.},
   Title = {In vivo implant mechanics of single-shaft microelectrodes in peripheral nervous tissue},
   BookTitle = {3rd Int'l Conference on Neural Engineering},
   Editor = {Akay, Metin},
   Address= {Hawaii, USA},
   Publisher = {IEEE},
      Year = {2007} } 

@ARTICLE{DaroltiCars,
  author = {Cristina Darolti and Ralf Bouchard and Stefan Farke and Alexandru P. Condurache and Ulrich Hofmann},
  title = {Measurement of Surgical Motion with a Marker Free Computer Vision
	Based System},
  journal = {International Journal of Computer Assisted Radiology and Surgery},
  year = {2007},
  volume = {2, S1},
pages = {S213},
  owner = {darolti},
  timestamp = {2007.07.13}
}

@inproceedings{MoshidisBMT07,
   Author = {Moshidis, Emmanouil and Matth{\"a}us, Lars and Besirevic, Armin and Schulz, Reinhard and Tronnier, Volker and Bonsanto, Matteo M. and Hofmann, Ulrich G.},
   Title = {Navigated intraoperative ultrasound imaging system},
   BookTitle = {BMT 2007},
   Address= {Aachen},
   Publisher = {de Gruyter},
      Year = {2007} } 

@ARTICLE{Maciak2007b,
  author = {Adam Maciak and Christian Kier and G{\"u}nter Seidel and Karsten Meyer-Wiethe
	and Ulrich Hofmann and Til Aach},
  title = {Robuste Ermittlung parametrischer Bilder für die Ultraschall-Perfusionsbildgebung
	basierend auf einem Modell der Boluskinetik von Kontrastmitteln},
  journal = {Biomedizinische Technik. Ergänzungsband.},
  year = {2007},
  volume = {52},
  pages = {Session A2},
  address = {Berlin},
  issn = {0939-4990},
  keywords = {perfuscope}
}

@InProceedings{ Hofmann06,
	title = {A novel high channel-count system for acute multi-site neuronal recordings},
	author = {U.G. Hofmann and A. Folkers and F. M{\"o}sch and T. Malina and K.M.L. Menne and M.G. Kindlundh and U. Thomas and D. Hoehl and G. Biella and E. De Schutter and P. Fagerstedt and K. Yoshida and W. Jensen and P. Norlin and M. de Curtis},
	year = {2006}
}

@Book{EberhardtArtrial2006,
  title     = {Atrial Near-Field and Ventricular Far-Field Analysis by Automated Signal Processing at Rest and During Exercise},
  publisher = {Wiley},
series    = {Ann Noninvasive Electrocardiol.},
  year      = {2006},
  author    = {Eberhardt, F. and Bonnemeier, H. and Lipphardt, M. and Hofmann, U.G. and Schunkert, H. and Wiegand, U.},
  volume    = {11, Issue 2},
  month     = { März}
}

@Article{Jensen06,
  author  = {Jensen, W. and Yoshida, K. and Hofmann, U.G.},
  title   = {Implant Mechanics of ACREO Silicon Electrodes in Rat Cerebral Cortex},
  journal = {IEEE Transactions on Bio-Medical Engineering},
  year    = {2006},
  month   = { Januar},
}