Paper Info
Title
Orientation histograms as shape priors for left ventricle segmentation using graph cuts.
Abstract
Poor contrast in magnetic resonance images makes cardiac left ventricle (LV) segmentation a very challenging task. We propose a novel graph cut framework using shape priors for segmentation of the LV from dynamic cardiac perfusion images. The shape prior information is obtained from a single image clearly showing the LV. The shape penalty is assigned based on the orientation angles between a pixel and all edge points of the prior shape. We observe that the orientation angles have distinctly different distributions for points inside and outside the LV. To account for shape change due to deformations, pixels near the boundary of the prior shape are allowed to change their labels by appropriate formulation of the penalty and smoothness terms. Experimental results on real patient datasets show our method's superior performance compared to two similar methods.
Year
DOI
Venue
2011
10.1007/978-3-642-23626-6_52
MICCAI (3)
Keywords
Field
DocType
orientation histogram,dynamic cardiac perfusion image,challenging task,shape change,prior shape,cardiac left ventricle,appropriate formulation,graph cut,shape prior information,shape prior,orientation angle,shape penalty,left ventricle segmentation
Cut,Computer vision,Histogram,Pattern recognition,Segmentation,Computer science,Active appearance model,Ventricle,Artificial intelligence,Pixel,Prior probability,Smoothness
Conference
Volume
Issue
ISSN
14
Pt 3
0302-9743
Citations 
PageRank 
References 
5
0.42
12
Authors
2
Name
Order
Citations
PageRank
Dwarikanath Mahapatra131233.71
Ying Sun222419.86