@inproceedings{9ea013c8441a424bafd1c385ada05250,
title = "Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors",
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.",
keywords = "3D shape analysis, Machine learning, Multiple sclerosis, Non-specific white matter, Spherical harmonics",
author = "Yeqi Wang and Madison Hansen and Darin Okuda and Andrew Wilson and Xiaohu Guo",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018 ; Conference date: 12-04-2018 Through 13-04-2018",
year = "2018",
month = apr,
day = "12",
doi = "10.1145/3191801.3191806",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "97--102",
booktitle = "Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018",
}