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Wismüller Lab

Welcome to the Wismüller Lab

The mission of Professor Wismüller's research group is to develop novel intuitively intelligible computational visualization methods for the exploratory analysis of high-dimensional data from biomedical imaging. Specifically, the focus of our research is on developing robust and adaptive systems for computer-aided analysis and visualization which combine principles and computational strategies inspired by biology with machine learning and image processing/computer vision approaches from electrical engineering and computer science.

Research efforts in Professor Wismüller's group are taking place at two complementary levels:

  • Mathematical algorithms for computational image analysis
  • Pattern recognition in clinical real-world applications

Application areas range from functional MRI for human brain mapping, MRI mammography for breast cancer diagnosis, image segmentation in Multiple Sclerosis and Alzheimer's Dementia to multi-modality fusion, biomedical time-series analysis, and quantitative bio-imaging. Professor Wismüller's laboratory is located in the Rochester Center for Brain Imaging, which houses a whole body 3T Siemens MRI Scanner and several high field magnets.


    1. Wismüller A
    2. Dsouza AM
    3. Vosoughi MA
    4. Abidin A
    Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data.; Scientific reports; Vol 11(1). 2021 Apr 09.
    1. Wismüller A
    2. Stockmaster L
    Investigating Covid-19 Pandemic-Induced Effects on Detection of Emergent Clinical Imaging Findings by Large-Scale Tracking of Utilization and Reading Results for AI-Based Image Analysis Services; Proc. of SPIE. Accepted for publication.. 2021 Jan 01.
    1. Wismüller A
    2. Vosoughi MA
    Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI); Proc. of SPIE. Accepted for publication.. 2021 Jan 01.
    1. Wismüller A
    2. DSouza AM
    3. Vosoughi MA
    4. Abidin AZ
    Network Connectivity Analysis in Complex Systems Using Large-Scale Non-Linear Granger Causality (lsNGC); Proc. of SPIE. Accepted for publication.. 2021 Jan 01.
    1. Vosoughi MA
    2. Wismüller A
    Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI; Proc. of SPIE. Accepted for publication.. 2021 Jan 01.
    1. Abidin AZ
    2. DSouza AM
    3. Schifitto G
    4. Wismüller A
    Detecting cognitive impairment in HIV-infected individuals using mutual connectivity analysis of resting state functional MRI.; Journal of neurovirology. 2020 Jan 07.
    1. Wismüller A
    2. Foxe JJ
    3. Saboksayr SS
    Large-Scale Extended Granger Causality (lsXGC) for Classication of Autism Spectrum Disorder from Resting-State Functional MRI; Proc. of SPIE; Vol 113141Y. 2020 Jan 01.

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Contact Us

  Wismüller Lab
601 Elmwood Ave, Box 608
Rochester, NY 14642