Improvement of Brain Tissue Segmentation Using Information Fusion Approach
M. Lamiche Chaabane. International Journal of Advanced Computer Science and Applications(IJACSA), (2011)
Zusammenfassung
The fusion of information is a domain of research in full effervescence these last years. Because of increasing of the diversity techniques of images acquisitions, the applications of medical images segmentation, in which we are interested, necessitate most of the time to carry out the fusion of various data sources to have information with high quality. In this paper we propose a system of data fusion through the framework of the possibility theory adapted for the segmentation of MR images. The fusion process is divided into three steps : fuzzy tissue maps are first computed on all images using Fuzzy C- Means algorithm. Fusion is then achieved for all tissues with a fusion operator. Applications on a brain model show very promising results on simulated data and a great concordance between the true segmentation and the proposed system.
%0 Journal Article
%1 IJACSA.2011.020612
%A Lamiche Chaabane, Moussaoui Abdelouahab
%D 2011
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K FCM; Information MR fusion; images. possibility segmentation; theory;
%N 6
%T Improvement of Brain Tissue Segmentation Using Information Fusion Approach
%U http://ijacsa.thesai.org/
%V 2
%X The fusion of information is a domain of research in full effervescence these last years. Because of increasing of the diversity techniques of images acquisitions, the applications of medical images segmentation, in which we are interested, necessitate most of the time to carry out the fusion of various data sources to have information with high quality. In this paper we propose a system of data fusion through the framework of the possibility theory adapted for the segmentation of MR images. The fusion process is divided into three steps : fuzzy tissue maps are first computed on all images using Fuzzy C- Means algorithm. Fusion is then achieved for all tissues with a fusion operator. Applications on a brain model show very promising results on simulated data and a great concordance between the true segmentation and the proposed system.
@article{IJACSA.2011.020612,
abstract = {The fusion of information is a domain of research in full effervescence these last years. Because of increasing of the diversity techniques of images acquisitions, the applications of medical images segmentation, in which we are interested, necessitate most of the time to carry out the fusion of various data sources to have information with high quality. In this paper we propose a system of data fusion through the framework of the possibility theory adapted for the segmentation of MR images. The fusion process is divided into three steps : fuzzy tissue maps are first computed on all images using Fuzzy C- Means algorithm. Fusion is then achieved for all tissues with a fusion operator. Applications on a brain model show very promising results on simulated data and a great concordance between the true segmentation and the proposed system.},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Lamiche Chaabane}, Moussaoui Abdelouahab},
biburl = {https://www.bibsonomy.org/bibtex/28477016159c7676ebc0c4c8b54b1a499/thesaiorg},
interhash = {f8781690df0578e60f538b2f742514e1},
intrahash = {8477016159c7676ebc0c4c8b54b1a499},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {FCM; Information MR fusion; images. possibility segmentation; theory;},
number = 6,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Improvement of Brain Tissue Segmentation Using Information Fusion Approach}},
url = {http://ijacsa.thesai.org/},
volume = 2,
year = 2011
}