PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Coppini G., Favilla R., Marraccini P., Moroni D., Pieri G. Quantification of epicardial fat by cardiac CT imaging. In: The Open Medical Informatics Journal, vol. 4 pp. 126 - 135. Special Issue: Intelligent Signal and Image Processing in eHealth. Ovidio Salvetti, Sara Colantonio (eds.). Bentham Open, 2010.
 
 
Abstract
(English)
The aim of this work is to introduce and design image processing methods for the quantitative analysis of epicardial fat by using cardiac CT imaging. Indeed, epicardial fat has recently been shown to correlate with cardiovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns still remain about the methods for measuring epicardial fat, its regional distribution on the myocardium and the accuracy and reproducibility of the measurements. In this paper, a method is proposed for the analysis of single-frame 3D images obtained by the standard acquisition protocol used for coronary calcium scoring. In the design of the method, much attention has been payed to the minimization of user intervention and to reproducibility issues. In particular, the proposed method features a two step segmentation algorithm suitable for the analysis of epicardial fat. In the first step of the algorithm, an analysis of epicardial fat intensity distribution is carried out in order to define suitable thresholds for a first rough segmentation. In the second step, a variational formulation of level set methods - including a specially-designed region homogeneity energy based on Gaussian mixture models- is used to recover spatial coherence and smoothness of fat depots. Experimental results show that the introduced method may be efficiently used for the quantification of epicardial fat.
URL: http://www.bentham-open.org/pages/content.php?TOMINFOJ/2010/00000004/00000003/126TOMINFOJ.SGM
DOI: 10.2174/1874431101004030126
Subject Image segmentation
Level set methods
Epicardial fat
Cardiac CT
I.4.6 Segmentation
I.4.9 Image Processing and Computer Vision. Applications
J.3 Life and Medical Sciences


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