Researchers and students at Pattern Recognition Lab (LME) work on the development and implementation of algorithms to classify and analyze patterns like images or speech. The research is mostly interdisciplinary and is focussed on medical- and health engineering. The LME has close national and international collaborations with other universities, research institutes and industrial partners. 

A summary of the projects at the Pattern Recognition Lab is available for download as a comprehensive brochure (PDF). 

• Research Areas
  

  • Pattern Recognition & Machine Learning

    

    Pattern Recognition deals with the automatic classification and analysis of sensor input data. In this area we have work groups in Speech Recognition and Understanding, Computer Vision, Multiple Criteria Optimization, Image Analysis, Image Segmentation, and Image Fusion.

    Medical Image Processing

    

    The research area medical image processing investigates formation and analysis of images in medicine. Currently the groups Analytic Reconstruction and Consistency, Computed Tomography: Algebraic Reconstruction and Motion, Magnetic Resonance Imaging, Phase-Contrast Imaging, and Ophthalmic Imaging are working on such topics.

    Big Data Applications

    

    With the sheer amount of "big data", new opportunities are available also for pattern recognition and machine learning. Examples are deep learning and precision learning. In this area, we have the groups of Learning Approaches for Medical Big Data Analysis, Population Modelling, and Enterprise Computing.

  Available Theses
  

  Extension of an audio-recordings database with features for similarity search (Master Arbeit) Betreuer: Maier, Andreas; Meyer-Wegener, Klaus

  Recent Publications
  

  Choi, Jang-hwan; McWalter, Emily; Datta, Sanjit; Müller, Kerstin; Maier, Andreas; Gold, Garry; Levenston, Marc; Fahrig, Rebecca: In Vivo 3D Measurement of Time-dependent Human Knee Joint Compression and Cartilage Strain During Static Weight-Bearing In: ORS 2016 Annual Meeting

  Jaremenko, Christian; Ravikumar, Nishant; Affronti, Emanuela; Merklein, Marion; Maier, Andreas: Determination of Forming Limits in Sheet Metal Forming using Deep Learning In: Materials

  Zarei, Shahab; Stimpel, Bernhard; Syben, Christopher; Maier, Andreas: User Loss A Forced-Choice-Inspired Approach to Train Neural Networks Directly by User Interaction In: Bildverarbeitung für die Medizin 2019

  Chen, Shuqing; Gehrer, Simone; Kaliman, Sara; Ravikumar, Nishant; Becit, Abdurrahman; Aliee, Maryam; Dudziak, Diana; Merkel, Rudolf; Smith, Ana-Sunćana; Maier, Andreas: Semi-Automatic Cell Correspondence Analysis Using Iterative Point Cloud Registration In: Bildverarbeitung für die Medizin 2019

  Wels, Michael; Lades, Félix; Muehlberg, Alexander; Suehling, Michael: General Purpose Radiomics for Multi-Modal Clinical Research In: Medical Imaging 2019: Computer-Aided Diagnosis

  Breininger, Katharina; Hanika, Moritz; Weule, Mareike; Kowarschik, Markus; Pfister, Marcus; Maier, Andreas: 3D-Reconstruction of Stiff Wires from a Single Monoplane X-Ray Image In: Workshop Bildverarbeitung für die Medizin

  Stromer, Daniel; Vetter, Andreas; Oezkan, Hasan Can; Probst, Christian; Maier, Andreas: Enhanced Crack Segmentation (eCS): A Reference Algorithm for Segmenting Cracks in Multicrystalline Silicon Solar Cells In: IEEE Journal of Photovoltaics

  Stimpel, Bernhard; Syben, Christopher; Würfl, Tobias; Breininger, Katharina; Lommen, Jonathan; Dörfler, Arnd; Maier, Andreas: Projection Image-to-Image Translation in Hybrid X-Ray/MR Imaging In: SPIE Medical Imaging 2019

  Schneider, Manuel; Janas, Gemini; Lugauer, Felix; Hoppe, Elisabeth; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R.: Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi-echo MRI In: Magn Reson Med

  Choi, Dahim; Kim, Juhee; Chae, Seung-Hoon; Kim, Byeongjoon; Baek, Jongduk; Maier, Andreas; Fahrig, Rebecca; Park, Hyun-Seok; Choi, Jang-Hwan: Multidimensional Noise Reduction in C-arm Cone-beam CT via 2D-based Landweber Iteration and 3D-based Deep Neural Networks In: SPIE Medical Imaging 2019
• Cooperations
  

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  LME-News
  

  Papal Charters Project in the News

  Our papal charter project was the focus of a recently published article from the weekend magazine of the NN/NZ (1.9.18), for which Klaus Herbers (Medieval History Lab, FAU) and Vincent Christlein (PRL) were interviewed.   It...[more]

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  LME Success at STACOM Atrial Segmentation Challenge

  

  Sulaiman Vesal, PhD student at the Pattern Recognition Lab made second place in the STACOM left atrial segmentation challenge at MICCAI 2018. The participants were required to produce a computational framework capable of...[more]

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  MICCAI Young Scientist Award

  

  Bastian Bier, PhD Student at our lab, in collaboration with Mathias Unberath of the Johns Hopkins University were awarded the prestigious 2018 MICCAI Young Scientist Award for their contribution titled "X-ray-transform...[more]

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  LME Successful at MIT Hackathon

  

  Daniel Stromer and Lennart Husvogt, both PhD students at the Pattern Recognition Lab, were part of one the winning teams of the MIT Media Lab's CityRobotics Hackathon. The hackathon focused on the intersection of transportation,...[more]

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  PRL on title of local newspaper

  

  On Wednesday 29th of August our lab made it onto the title of the NZ with the European Time Machine Project. An Interview with more details is found on Page 20. Have fun reading the news![more]

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  "Medical Imaging Systems - An Introductory Guide" published

  

  It is a great pleasure to announce that our new text book "Medical Imaging Systems - An Introductory Guide“ has been published as open access version under this...[more]

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