Automatic Multiple Sclerosis Detection Based on Integrated Square Estimation
Abstract
This paper presents a fully automatic method for segmentation of multiple sclerosis (MS) lesions from multiple sequence MR (T2-weighted and FLAIR) images. Our method treats MS lesions as outliers to the normal brain tissue distribution, and the separation is achieved by minimizing a statistically robust L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> E measure, which is defined as the squared difference between the true density and the assumed Gaussian mixture. Pre- and post-processing procedures including intensity normalization and false positive pruning are designed to remove various signal artifacts. Our method is fully automatic and doesn't require any training, atlas or thresholding steps. The results of our method are compared with lesion delineations by human experts, and a high classification accuracy is demonstrated on 16 datasets containing small to moderate lesion loads.
Cite
Text
Liu et al. "Automatic Multiple Sclerosis Detection Based on Integrated Square Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204351Markdown
[Liu et al. "Automatic Multiple Sclerosis Detection Based on Integrated Square Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/liu2009cvprw-automatic/) doi:10.1109/CVPRW.2009.5204351BibTeX
@inproceedings{liu2009cvprw-automatic,
title = {{Automatic Multiple Sclerosis Detection Based on Integrated Square Estimation}},
author = {Liu, Jundong and Smith, Charles D. and Chebrolu, Hima},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2009},
pages = {31-38},
doi = {10.1109/CVPRW.2009.5204351},
url = {https://mlanthology.org/cvprw/2009/liu2009cvprw-automatic/}
}