Brain tissue segmentation based on diffusion MRI using ℓ0 sparse-group representation classification

Pew Thian Yap, Yong Zhang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

We present a method for automated brain tissue segmentation based on diffusion MRI. This provides information that is complementary to structural MRI and facilitates fusion of information between the two imaging modalities. Unlike existing segmentation approaches that are based on diffusion tensor imaging (DTI), our method explicitly models the coexistence of various diffusion compartments within each voxel owing to different tissue types and different fiber orientations. This results in improved segmentation in regions with white matter crossings and in regions susceptible to partial volume effects. For each voxel, we tease apart possible signal contributions from white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) with the help of diffusion exemplars, which are representative signals associated with each tissue type. Each voxel is then classified by determining which of the WM, GM, or CSF diffusion exemplar groups explains the signal better with the least fitting residual. Fitting is performed using ℓ0 sparse-group approximation, circumventing various reported limitations of ℓ1 fitting. In addition, to promote spatial regularity, we introduce a smoothing technique that is based on ℓ0 gradient minimization, which can be viewed as the ℓ0 version of total variation (TV) smoothing. Compared with the latter, our smoothing technique, which also incorporates multi-channelWM,GM, and CSF concurrent smoothing, yields marked improvement in preserving boundary contrast and consequently reduces segmentation bias caused by smoothing at tissue boundaries. The results produced by our method are in good agreement with segmentation based on T1-weighted images.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
EditorsAlejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
PublisherSpringer Verlag
Pages132-139
Number of pages8
ISBN (Print)9783319245737
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 52015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period15/10/515/10/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yap, P. T., Zhang, Y., & Shen, D. (2015). Brain tissue segmentation based on diffusion MRI using ℓ0 sparse-group representation classification. In A. F. Frangi, N. Navab, J. Hornegger, & W. M. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings (pp. 132-139). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351). Springer Verlag. https://doi.org/10.1007/978-3-319-24574-4_16