Document Detail


A modality-agnostic patch-based technique for lesion filling in multiple sclerosis.
MedLine Citation:
PMID:  25485451     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
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.
Authors:
Ferran Prados; Manuel Jorge Cardoso; David MacManus; Claudia A M Wheeler-Kingshott; Sébastien Ourselin
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention     Volume:  17     ISSN:  -     ISO Abbreviation:  Med Image Comput Comput Assist Interv     Publication Date:  2014  
Date Detail:
Created Date:  2014-12-08     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101249582     Medline TA:  Med Image Comput Comput Assist Interv     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  781-8     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
(UK)/MR/J01007X/1//Medical Research Council

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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