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Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting.

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.

journal_name

J Healthc Eng

journal_title

Journal of healthcare engineering

authors

Zlokolica V,Krstanović L,Velicki L,Popović B,Janev M,Obradović R,Ralević NM,Jovanov L,Babin D

doi

10.1155/2017/5817970

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

5817970

eissn

2040-2295

issn

2040-2309

journal_volume

2017

pub_type

杂志文章

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