TY - CHAP
T1 - A modality-agnostic patch-based technique for lesion filling in multiple sclerosis
AU - Prados, Ferran
AU - Cardoso, Manual Jorge
AU - MacManus, David
AU - Wheeler-Kingshott, Claudia A.M.
AU - Ourselin, Sébastien
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Multiple Sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. With the aim of reducing this bias, existing techniques fill segmented lesions as normal appearing white matter. However, due to lesion segmentation errors or the presence of neighbouring structures, such as the ventricles and deep grey matter structures, filling all lesions as white matter like intensities is prone to introduce errors and artefacts. In this paper, we present a novel lesion filling strategy based on in-painting techniques for image completion. This technique makes use of a patch-based Non-Local Means algorithm that fills the lesions with the most plausible texture, rather than normal appearing white matter. We demonstrate that this strategy introduces less bias and fewer artefacts and spurious edges than previous techniques. The advantages of the proposed methodology are that it preserves both anatomical structure and signal-to-noise characteristics even when the lesions are neighbouring grey matter and cerebrospinal fluid, and avoids excess blurring or rasterisation due to the choice of segmentation plane, and lesion shape, size and/or position.
AB - Multiple Sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. With the aim of reducing this bias, existing techniques fill segmented lesions as normal appearing white matter. However, due to lesion segmentation errors or the presence of neighbouring structures, such as the ventricles and deep grey matter structures, filling all lesions as white matter like intensities is prone to introduce errors and artefacts. In this paper, we present a novel lesion filling strategy based on in-painting techniques for image completion. This technique makes use of a patch-based Non-Local Means algorithm that fills the lesions with the most plausible texture, rather than normal appearing white matter. We demonstrate that this strategy introduces less bias and fewer artefacts and spurious edges than previous techniques. The advantages of the proposed methodology are that it preserves both anatomical structure and signal-to-noise characteristics even when the lesions are neighbouring grey matter and cerebrospinal fluid, and avoids excess blurring or rasterisation due to the choice of segmentation plane, and lesion shape, size and/or position.
UR - http://www.scopus.com/inward/record.url?scp=84906971643&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10470-6_97
DO - 10.1007/978-3-319-10470-6_97
M3 - Conference paper
AN - SCOPUS:84906971643
SN - 9783319104690
VL - 8674 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 781
EP - 788
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PB - Springer Verlag
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Y2 - 14 September 2014 through 18 September 2014
ER -