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Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/4177

Title: Incorporating Domain Knowledge into the Fuzzy Connectedness Framework: Application to Brain Lesion Volume Estimation in Multiple Sclerosis.
Authors: Horsfield, Mark A.
Bakshi, Rohit
Rovaris, Marco
Rocca, Maria A.
Dandamundi, Venkata S.R.
Valsasina, Paola
Judica, Elda
Lucchini, Fulvio
Guttmann, Charles R.G.
Sormani, Maria Pia
Filippi, Massimo
Issue Date: Dec-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE).
Citation: IEEE Transactions on Medical Imagining, 2007, 26 (12), pp. 1670-1680.
Abstract: A method for incorporating prior knowledge into the fuzzy connectedness image segmentation framework is presented. This prior knowledge is in the form of probabilistic feature distribution and feature size maps, in a standard anatomical space, and "intensity hints" selected by the user that allow for a skewed distribution of the feature intensity characteristics. The fuzzy affinity between pixels is modified to encapsulate this domain knowledge. The method was tested by using it to segment brain lesions in patients with multiple sclerosis, and the results compared to an established method for lesion outlining based on edge detection and contour following. With the fuzzy connections (FC) method, the user is required to identify each lesion with a mouse click, to provide a set of seed pixels. The algorithm then grows the features from the seeds to define the lesions as a set of objects with fuzzy connectedness above a pre-set threshold. The FC method gave improved inter-observer reproducibility of lesion volumes, and the set of pixels determined to be lesion was more consistent compared to the contouring method. The operator interaction time required to evaluate one subject was reduced from an average of 111 minutes with contouring to 16 minutes with the FC method.
ISSN: 0278-0062
Links: http://ieeexplore.ieee.org/
http://dx.doi.org/10.1109/TMI.2007.901431
http://hdl.handle.net/2381/4177
Type: Article
Description: This is the author's final draft of the paper published as IEEE Transactions on Medical Imagining, 2007, 26 (12), pp. 1670-1680, Copyright © 2007 IEEE. The final version is available from http://ieeexplore.ieee.org/. Doi: 10.1109/TMI.2007.901431. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Leicester’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Appears in Collections:Published Articles, Dept. of Cardiovascular Sciences

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