@inproceedings{7bfa9fc8d45b4072a8175f3fc7032c09,
title = "Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions",
abstract = "This paper presents a novel methodology based on joint histograms, for the automated and unsupervised segmentation of multiple sclerosis (MS) lesion in cranial magnetic resonance (MR) imaging. Our workflow is composed of three steps: locate the MS lesion region in the joint histogram, segment MS lesions, and false positive reduction. The advantage of our approach is that it can segment small lesions, does not require prior skull segmentation, and is robust with regard to noisy and inhomogeneous data. Validation on the BrainWeb simulator and real data demonstrates that our method has an accuracy comparable with other MS lesion segmentation methods.",
author = "Ziming Zeng and Reyer Zwiggelaar",
year = "2011",
month = sep,
day = "23",
doi = "10.1007/978-3-642-24136-9_12",
language = "English",
isbn = "978-3-642-24135-2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "133--144",
booktitle = "Computer Vision/Computer Graphics Collaboration Techniques - 5th International Conference, MIRAGE 2011, Proceedings",
address = "Switzerland",
}