White matter fiber set simplification by redundancy reduction with minimum anatomical information loss

Gali Zimmerman Moreno*, Guy Alexandroni, Hayit Greenspan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Advanced Diffusion Weighted Imaging (DWI) techniques and leading tractography algorithms produce dense fiber sets of hundreds of thousands of fibers, or more. In order to make fiber based analysis more practical, the fiber set needs to be preprocessed to eliminate redundancies and to keep only essential representative fibers. In this paper we evaluate seven commonly used distance metrics for fiber clustering and present a novel approach for comparing the metrics as well as estimating the anatomical information loss as a function of the reduction rate. The framework includes pre-clustering into sub-groups using K-means, followed by further decomposition using Hierarchical Clustering, each time with a different distance metric. Finally, volume histograms comparison is used to compare the reduction quality with the different metrics. The proposed comparison was applied to a dataset containing tractographies of four healthy individuals. Each set contains around 600k fibers.

Original languageEnglish
Title of host publicationComputational Diffusion MRI - MICCAI Workshop, 2015
EditorsYogesh Rathi, Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Marco Reisert
PublisherSpringer Heidelberg
Number of pages12
ISBN (Print)9783319285863
StatePublished - 2016
EventWorkshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany
Duration: 9 Oct 20159 Oct 2015

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X


ConferenceWorkshop on Computational Diffusion MRI, MICCAI 2015


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