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
  

  New PhD Student

  Andreas Siegling joined the Pattern Recognition Lab on May 1st and is part of the Enterprise Computing Group.  He received his Bachelor and Master of Science in Computer Science from Friedrich-Alexander-Universität...[more]

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  PhD Position in cooperation with Medical Data Donors

  

  We offer a PhD stipend for 3 years in cooperation with Medical Data Donors:   We aim at developing the first levels of the crowd game and methods how to create deep learning segmentation algorithms that are created...[more]

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  Start of the Luther project in the news

  

  The start of the project "Critical Catalog of the Luther-Portraits (1519-1530)" at June 1st received positive media response from the Nürnberger Zeitung. The interdisciplinary project which is in corporation with the...[more]

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  Invited Talk by Sina Farsiu - July 13th

  

  Sina Farsiu (Duke Biomedical Engineering - Duke University) will visit our lab on Friday 13th of July and give a presentation at 10 AM in lecture hall H15 entitled "Fundamental Limits of Resolution and Segmentation Accuracy...[more]

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  New PhD Student

  

  Julian Hoßbach joined the Pattern Recognition Lab on May 14 and is part of the Magnetic Resonance Imaging group. He received his Bachelor and Master of Science in Biomedical Engineering from the Friedrich-Alexander University...[more]

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  LME Successful at Rural Health Hackathon

  

  Tobias Würfl, PhD student at the Pattern Recognition Lab and his team won an award for the best business idea at the hacking rural health hackathon (link). The hackathon was focussed on specific healthcare challenges arising in...[more]

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