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

  Image Analysis for Microscopical Layer Characterization and Morphological Feature Parametrization (Master Arbeit) Betreuer: Maier, Andreas; Steidl, Stefan; Spies, Ines

  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

  Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Xia, Yan; Choi, Jang‐Hwan; Chen, Shuqing; Hu, Shiyang; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas: A learning-based material decomposition pipeline for multi-energy x-ray imaging In: Medical Physics

  Mirzaalian Dastjerdi, Houman; Töpfer, Dominique; Rupitsch, Stefan; Maier, Andreas: Measuring Surface Area of Skin Lesions with 2D and 3D Algorithms In: International Journal of Biomedical Imaging

  Seewald, Lucas Adams; Rodrigues, Vinicius Facco; Ollenschläger, Malte; Stoffel Antunes, Rodolfo; da Costa, Cristiano Andre; sa Rosa Righi, Rodrigo; de Silveira, Luiz Gonzaga; Maier, Andreas; Eskofier, Björn; Fahrig, Rebecca: Toward analyzing mutual interference on infrared-enabled depth cameras In: Copmuter Vision and Image Understanding

  Bayer, Siming; Wydra, Adrian; Zhong, Xia; Ravikumar, Nishant; Strumia, Maddalena; Schaffert, Roman; Ostermeier, Martin; Fahrig, Rebecca; Maier, Andreas: An anthropomorphic deformable phantom for brain shift simulation In: IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

  Kurzendorfer, Tanja: Fully Automatic Segmentation of Anatomy and Scar from LGE-MRI In:

  Lu, Yanye; Kowarschik, Markus; Huang, Xiaolin; Chen, Shuqing; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas: Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging In: IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES

  Husvogt, Lennart; Arya, Malvika; Alibhai, A. Yasin; Moult, Eric; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas: Automatic detection of capillary dilation and looping in patients with diabetic retinopathy from optical coherence tomography angiography data In: ARVO Annual Meeting 2018

  Bayer, Siming; Wydra, Adrian; Zhong, Xia; Ravikumar, Nishant; Strumia, Maddalena; Schaffert, Roman; Ostermeier, Martin; Fahrig, Rebecca; Maier, Andreas: An anthropomorphic deformable phantom for brain shift simulation In: IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

  Meister, Felix; Passerini, Tiziano; Mihalef, Viorel; Tuysuzoglu, Ahmet; Maier, Andreas; Mansi, Tommaso: Towards Fast Biomechanical Modeling of Soft Tissue Using Neural Networks In: Medical Imaging Meets NeurIPS
• Cooperations
  

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  LME-News
  

  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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  New PhD Student

  

  Aline Sindel joined the Pattern Recognition Lab on April 1st and is part of the Computer Vision Group. She received her Bachelor and Master of Science in Medical Engineering from the Friedrich-Alexander-Universität...[more]

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  DAS 2018: Best Student Paper Award

  

  Daniel Stromer, PhD student at the Pattern Recognition Lab, has received the Best Student Paper Award at the 13th IAPR International Workshop on Document Analysis Systems (DAS 2018) for his publication entitled ...[more]

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  Students Receive Newcomer Awards

  

  Elisabeth Hoppe (right), PhD candidate at the Pattern Recognition Lab and Maike Stöve (left), a former student of the Pattern Recognition Lab, made the second and third places at the “Connecting Healthcare IT” Newcomer Award for...[more]

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