Authors: Zlokolica, Vladimir
Krstanović, Lidija
Velicki, Lazar
Popović, Branislav
Janev, Marko 
Obradović, Ratko
Ralević, Nebojša
Jovanov, Ljubomir
Babin, Danilo
Title: Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting
Journal: Journal of Healthcare Engineering
Volume: 2017
Issue Date: 1-Jan-2017
Rank: M23
ISSN: 2040-2295
DOI: 10.1155/2017/5817970
Abstract: 
Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods. The problem of the state-of-the-art methods for epicardial fat segmentation is their high dependency on user interaction, resulting in low reproducibility of studies and time-consuming analysis. We propose in this paper a novel semiautomatic approach for segmentation and quantification of epicardial fat from 3D CT images. Our method is a semisupervised slice-by-slice segmentation approach based on local adaptive morphology and fuzzy c-means clustering. Additionally, we use a geometric ellipse prior to filter out undesired parts of the target cluster. The validation of the proposed methodology shows good correspondence between the segmentation results and the manual segmentation performed by physicians.
Publisher: Hindawi
Project: Provincial Secretariat for Science and Technological Development of the Autonomous Province of Vojvodina, Grant no. 114-451-873/2015-01

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