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Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming
, Chandrasekharan A,
Published in Springer Science and Business Media LLC
Volume: 15
Issue: 6
Pages: 673 - 688
The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method. © 2018, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetInternational Journal of Automation and Computing
PublisherData powered by TypesetSpringer Science and Business Media LLC
Open Access0