incorporating global information into active contour models
DESCRIPTION
Incorporating Global Information into Active Contour Models. Anthony Yezzi Georgia Institute of Technology. Snakes: Active Contour Models. Initialization. Final Segmentation. Snakes or Active Contours pose the segmentation as an energy minimization problem. Kass, Witkins & Terzopoulos. - PowerPoint PPT PresentationTRANSCRIPT
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Incorporating Global Information into Active Contour Models
Anthony YezziGeorgia Institute of Technology
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Snakes: Active Contour Models Snakes or Active Contours pose the segmentation as an
energy minimization problem. Kass, Witkins & Terzopoulos.
Snake ext intC C
E E dp E dp
Initialization Final Segmentation
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Local Minima One major drawback of Active Contour model is the
tendency to get stuck in “Local minima” caused by subtle irrelevant edges and image features.
Initialization Final Segmentation
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Avoiding Local Minima Balloon Force: (Cohen)
Makes assumption about the initialization. Biased final segmentation result.
Region-based Energy:
Makes Strong assumptions about the image. Global minimum of Edge-based Energy:
Global minimal path for open curves/geodesics. (Cohen & Kimmel) Not suitable for closed curves (Geodesic Active Contours used instead)
( )EdgeC
E s ds
Reg in outinside outsideC C
E F dA F dA
Image Domain2( )inI 2( )outI
BalC C
E Eds ds
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Active GeodesicsRegion-based active contour segmentation with a Global Edge-based Constraint
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Edge-based Segmentation Globally Optimal Geodesic Active Contours -
(GOGAC) Appleton B. and Talbot H.
Introduce an artificial cut in the image domain and search for an optimal open geodesic with end points on either side of the cut.
GOGAC Propagating FrontsTest Image
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Purely Region-Based Segmentation Region-based energy minimization.
Chan-Vese Model (Mumford-Shah special case)2 2( ) ( )Reg in out
inside outsideC C
E I dA I dA
Initialization Final Segmentation
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Incorporating Region-based Energy in Edge-based Segmentation
Test Image Propagating Fronts
Saddle Points Associated ClosedCurves
Closed Curve withleast Region-based
Energy
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Active Geodesics
Minimize the region-based energy and restrict evolution to a single local degree of freedom: translation of saddle point in the normal direction to the curve at that point.
Initialization away from object boundary
Reverse roles ofSource/saddle point
Rep Reg gC
E F ds
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Continuum of “Closed” geodesics
Test Image
SegmentationPropagating Fronts1/ (|| || 1)I
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Region-based EvolutionMove Saddlepoint
Segmentation after2nd iterationPropagating FrontsNew Source
Segmentation after3rd iteration
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Evolution (Left Ventricle Segmentation)
Iterations – 4 to 18
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Right Ventricle Segmentation User can interact with the segmentation algorithm by
adding poles and zeros, to attract and repel the contour towards desired edges.
Red ‘X’ – Additional Pole (Repeller) Green ‘X’ – Additional Zero (Attractor)
Initial Right Ventricle Segmentation with Active Geodesics
Segmentation afteradding a repeller
Final segmentation with 2 repellers and
1 attractor
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Cell Segmentation
Edge-based GOGAC segmentation for three different initializations
Active geodesic-based segmentation with three different initializations
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Nuclei Segmentation
Nuclei segmentation with same initialization as the previous slide
Region-based Chan-Vese segmentation for nucleus segmentation