variational approaches and image segmentation lecture #8
DESCRIPTION
Variational Approaches and Image Segmentation Lecture #8. Hossam Abdelmunim 1 & Aly A. Farag 2 1 Computer & Systems Engineering Department, Ain Shams University, Cairo, Egypt 2 Electerical and Computer Engineering Department, University of Louisville, Louisville, KY, USA ECE 643 – Fall 2010. - PowerPoint PPT PresentationTRANSCRIPT
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Variational Approaches and Image Segmentation
Lecture #8Lecture #8Hossam Abdelmunim1 & Aly A. Farag2
1Computer & Systems Engineering Department, Ain Shams University, Cairo, Egypt
2Electerical and Computer Engineering Department, University of Louisville, Louisville, KY, USA
ECE 643 – Fall 2010
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Adaptive Multi-modal SegmentationAdaptive Multi-modal Segmentation
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OutlineOutline
• Multiple region representation.Multiple region representation.
• Energy function formulation for bimodal and Energy function formulation for bimodal and multi modal cases.multi modal cases.
• Adaptive region model PDE’s.Adaptive region model PDE’s.
• Initialization.Initialization.
• Experimental resultsExperimental results
• Conclusion and criticism.Conclusion and criticism.
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Related PapersRelated Papers
T. Brox and J. Weickert. ”Level Set Based Image Segmentation with Multiple Regions,” in Pattern Recognition., Springer LNCS 3175, pp. 415–423, Aug. 2004.
A. A. Farag and Hossam Hassan, “Adaptive Segmentation of Multi-modal 3D Data Using Robust Level Set Techniques“, in Proc International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI’04), Saint Malo,France, pp. 143-150, September, 2004.
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Regions RepresentationRegions Representation
Assume that we have image I with K classes (regions).
K (i=1..K) level set functions are defined to represent the regions:
outsideifXD
boundarytheon
insideifXD
Xi
)(
0
)(
)(
D is the minimum Euclidean distance between the current point and the contour/surface.
The positive part of the level set function is dedicated for the associated region. It is adaptive because the contour changes with time.
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Segmentation ObjectivesSegmentation Objectives
K contours are initialized.
They are required to evolve to hit the boundaries of their associated regions.
Level sets change to minimize a given energy function.
The steady state solution will represent segmented regions in the positive part of each function.
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Image and FeatureImage and Feature
Color intensityTexture tensor
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Adaptive Region ParametersAdaptive Region Parameters
Regions statistics are described by Gaussian models.
The parameters are estimated by M.L.E as follows:
The prior probability is estimated as the region area:
dxH
IdxH
i
i
i))((
))((
dxH
dxIxIxH
i
Tiii
i))((
))()()())(((
K
ii
i
i
dxH
dxH
1
))((
))((
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Automatic Seed InitializationAutomatic Seed Initialization
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Results (Natural Image)Results (Natural Image)
Image Size: 200 X 276
Window Size: 15 X 15
Two Classes
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Results (MRI-T1 image)Results (MRI-T1 image)
Image Size: 256 X 256
Window Size: 5 X 5
Two Classes
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Results (MRI-PD Image)Results (MRI-PD Image)
Image Size: 375 X 373
Window Size: 5 X 5
Three Classes
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Results (Synthetic)Results (Synthetic)
Image Size: 300 X 150
Window Size: 25 X 25
Three Classes
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Results (Color Image)Results (Color Image)
Image Size: 342 X 450
Window Size: 35 X 35
Two Classes
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Results (Continue)Results (Continue)
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DiscussionsDiscussions
An adaptive multi region segmentation approach is proposed.
This method is very suitable for the homogeneous regions.
The regularization term in the PDE enable segmenting images with noise. In case of high noise levels, the convergence time increases and the boundaries are miss-classified by increasing the strength of the curvature component.
Synthetic, real, and medical examples are given.
Non parametric probability density functions may be investigated replacing the Gaussian models.
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Thank You&
Questions
Thank You&
Questions