activity
20132023
most citedMulti-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization

5 citations · 11 across the 7 of their papers we have counts for

collaborators

8 papers

eess.IV2023

Anisotropic Fanning Aware Low-Rank Tensor Approximation Based Tractography

Johannes Grün, Jonah Sieg, Thomas Schultz

Low-rank higher-order tensor approximation has been used successfully to extract discrete directions for tractography from continuous fiber orientation density functions (fODFs). H…

eess.IV2022★ 4 cited

Combining Image Space and q-Space PDEs for Lossless Compression of Diffusion MR Images

Ikram Jumakulyyev, Thomas Schultz

Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire microstructural information by measuring water self-diffusion at the voxel level. However, it gener…

cs.CV2020

Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression

Ikram Jumakulyyev, Thomas Schultz

Edge-enhancing diffusion (EED) can reconstruct a close approximation of an original image from a small subset of its pixels. This makes it an attractive foundation for PDE based im…

stat.AP2019

RefCurv: A Software for the Construction of Pediatric Reference Curves

Christian Winkler, Katharina Linden, Andreas Mayr +4

In medicine, reference curves serve as an important tool for everyday clinical practice. Pediatricians assess the growth process of children with the help of percentile curves serv…

cs.GR2017

Visual Analytics of Group Differences in Tensor Fields: Application to Clinical DTI

Amin Abbasloo, Vitalis Wiens, Tobias Schmidt-Wilcke +3

We present a visual analytics system for exploring group differences in tensor fields with respect to all six degrees of freedom that are inherent in symmetric second-order tensors…

cs.LG2017★ 2 cited

Classification on Large Networks: A Quantitative Bound via Motifs and Graphons

Andreas Haupt, Mohammad Khatami, Thomas Schultz +1

When each data point is a large graph, graph statistics such as densities of certain subgraphs (motifs) can be used as feature vectors for machine learning. While intuitive, motif…