Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors

Yeqi Wang, Madison Hansen, Darin Okuda, Andrew Wilson, Xiaohu Guo

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

Abstract

Multiple Sclerosis (MS) and Non-Specific White Matter (NSWM) lesion classification is a traditional problem in neurology. In this paper, we propose a machine learning method to predict MS/NSWM lesion types based on segmented 3D lesion models from clinical MRI images. A spherical harmonics shape descriptor is used to express lesion shape as feature vectors. We generate a parametrization mapping from a 3D lesion to a sphere and then extract spherical harmonics features as descriptor based on that mapping. This descriptor conveys shape difference properties of MS/NSWM lesion which can be trained to predict unknown lesions using machine learning models such as boosting trees, support vector machines (SVMs), logistic regression, and so on. Experiments demonstrate that our 3D model feature representation enables significant performance on MS/NSWM lesions in their classification.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018
PublisherAssociation for Computing Machinery
Pages97-102
Number of pages6
ISBN (Electronic)9781450354394
DOIs
StatePublished - Apr 12 2018
Event3rd International Workshop on Interactive and Spatial Computing, IWISC 2018 - Richardson, United States
Duration: Apr 12 2018Apr 13 2018

Other

Other3rd International Workshop on Interactive and Spatial Computing, IWISC 2018
CountryUnited States
CityRichardson
Period4/12/184/13/18

Fingerprint

Learning systems
Neurology
Magnetic resonance imaging
Support vector machines
Logistics
Experiments

Keywords

  • 3D shape analysis
  • Machine learning
  • Multiple sclerosis
  • Non-specific white matter
  • Spherical harmonics

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Wang, Y., Hansen, M., Okuda, D., Wilson, A., & Guo, X. (2018). Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors. In Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018 (pp. 97-102). Association for Computing Machinery. https://doi.org/10.1145/3191801.3191806

Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors. / Wang, Yeqi; Hansen, Madison; Okuda, Darin; Wilson, Andrew; Guo, Xiaohu.

Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018. Association for Computing Machinery, 2018. p. 97-102.

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

Wang, Y, Hansen, M, Okuda, D, Wilson, A & Guo, X 2018, Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors. in Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018. Association for Computing Machinery, pp. 97-102, 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018, Richardson, United States, 4/12/18. https://doi.org/10.1145/3191801.3191806
Wang Y, Hansen M, Okuda D, Wilson A, Guo X. Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors. In Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018. Association for Computing Machinery. 2018. p. 97-102 https://doi.org/10.1145/3191801.3191806
Wang, Yeqi ; Hansen, Madison ; Okuda, Darin ; Wilson, Andrew ; Guo, Xiaohu. / Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors. Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018. Association for Computing Machinery, 2018. pp. 97-102
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