DocumentCode :
672640
Title :
Two Statistical Mixture Model vs. Fuzzy C-Means: In the application of edema segmentation
Author :
Kadir, Kushsairy ; Hao Gao ; Payne, Alex R. ; Soraghan, John ; Berry, Colin
Author_Institution :
British Malaysian Inst., Univ. Kuala Lumpur, Gombak, Malaysia
fYear :
2013
fDate :
8-10 Oct. 2013
Firstpage :
333
Lastpage :
336
Abstract :
Evaluating salvageable myocardial after myocardial infarction (MI) is an important prognosis in the follow up study of MI. Since the extent of myocardial edema delineates the ischemic area-at-risk (AAR) after MI the AAR can be used to estimate the amount of salvageable myocardial post-MI and therefore has potential clinical utility in the management of acute MI patients. Two methods for the segmentation and quantification of edema from T2 weighted MRI data have been presented. The methods presented in this paper are Two Statistical Mixture Model and Fuzzy C-means. Quantitative evaluations of segmentation accuracy for the two algorithms were performed by comparing to manual segmentation on real T2 weighted CMR data collected from Golden Jubilee National Hospital, Glasgow for 16 adult subjects.
Keywords :
biomedical MRI; image segmentation; medical image processing; pattern clustering; Glasgow; Golden Jubilee National Hospital; T2 weighted MRI data; area-at-risk; clinical utility; edema segmentation; fuzzy C-means; myocardial edema; myocardial infarction; prognosis; salvageable myocardial post-MI; statistical mixture model; Image segmentation; Manuals; Myocardium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal and Image Processing Applications (ICSIPA), 2013 IEEE International Conference on
Conference_Location :
Melaka
Print_ISBN :
978-1-4799-0267-5
Type :
conf
DOI :
10.1109/ICSIPA.2013.6708028
Filename :
6708028
Link To Document :
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