Download - CSCE790T Medical Image Processing
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12-Apr-2007 1
CSCE790T Medical Image Processing
University of South Carolina Department of Computer Science
3D Active Shape Models Integrating Robust Edge Identification and
Statistical Shape Models
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12-Apr-2007 2
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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12-Apr-2007 3
Introduction
• Collaboration with UNC departments of computer science, and psychiatry
• Submitted to MICCAI 07
• Propose two new strategies to improve 3D ASM performance:
– Developing a robust edge-identification algorithm to reduce the risk of detecting false edges
– Integrating the edge-fitting error and statistical shape model defined by a PDM into a unified cost function
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12-Apr-2007 4
Introduction
• Apply the proposed ASM to the challenging tasks of detecting the left hippocampus and caudate surfaces from an subset of 3D pediatric MR images
• Compare its performance with a recently reported atlas based method.
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12-Apr-2007 5
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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12-Apr-2007 6
Motivation
• Segmentation facilitates the discovery of diseased structures in medical images
• Two neurological shape structures of interest– Caudate Nucleus
• body movement and coordination • cauda (tail)
– Hippocampus• memory and coordination• hippo (horse) and Kampi (curve)
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12-Apr-2007 7
Motivation
http://www.emedicine.com/radio/topic443.htm#target2
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12-Apr-2007 8
Motivation
http://www.sci.uidaho.edu/med532/basal.htm
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12-Apr-2007 9
Motivation
• Hippocampus, and Caudate related to the following areas of research:– Epileptic seizures (MTS)– Alzheimer disease– Amnesic syndromes– Schizophrenia– Parkinson's disease– Huntington's disease
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12-Apr-2007 10
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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12-Apr-2007 11
General ASM Algorithm
• Initial placement of point distribution model (PDM) mean shape inside image volume T (v : s, t, )
• Generate gradient magnitude values for each voxel location in 3D image volume
• while not(convergence)– Identify strongest edge for each landmark point along its
search path– Using this edge information determine new ASM shape– Update PDM global transform T(s, t, ) and local transform
variables– Verify new ASM shape with PDM shape space limits– If global, and local transform variables are not longer
changing ASM has converged
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12-Apr-2007 12
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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12-Apr-2007 13
Robust Edge Detection
• Identify boundary edges of desired surface structure inside image volume
• Each edge is represented by an gradient magnitude value
• Stronger edges have larger gradient magnitude values
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12-Apr-2007 14
Robust Edge Detection
Example sagittal plane edges for hippocampus
Image slice
Gradient magnitude slice
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12-Apr-2007 15
Robust Edge Detection
Example coronal plane edges for hippocampus
Image slice
Gradient magnitude slice
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12-Apr-2007 16
Robust Edge Detection
• Boundary edges are identified along search paths for each landmark point
• Search paths are defined by profile locations () along each landmark points normal vector
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12-Apr-2007 17
Robust Edge Detection
• Additionally, each landmark points normal vector is determined by the surface mesh
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12-Apr-2007 18
Robust Edge Detection
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12-Apr-2007 19
Robust Edge Detection
• Generally, edges detection along search paths are considered dangerous
• Subject to noise
• Spurious (false) edges
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12-Apr-2007 20
Robust Edge Detection
• Propose an new neighborhood solution• Spatially consistent profile location (i
)• Reduces the likelihood of an false edge
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12-Apr-2007 21
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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12-Apr-2007 22
Unified Cost Function
• Traditionally each of the models local transform variables (bi) are updated after the ASM shape is found
• If the ASM shape (u) is not defined within the limits of the PDM shape space the local transform variables (bi)
are rescaled appropriately• Shape information may be lost• Re-active solution
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12-Apr-2007 23
Unified Cost Function
• Steps in shape deformation where ASM shape not within PDM shape space limits
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12-Apr-2007 24
Unified Cost Function
• Proposed solution implemented by an unified cost function
• Pro-active solution• Efficiently solved as an quadratic programming
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12-Apr-2007 25
Unified Cost Function
• The cost function can be viewed as,
Tt
T vαuvαuαααα N D N min 1-*t*
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• vT = (3nx1) vector global transformed mean shape• DT -1 = (3nx3n) matrix global transformed inverse covariance matrix• u = (3nx1) vector initial PDM mean shape or previous ASM shape• N = (3nxn) matrix the normal vectors• * = (nx1) vector profile locations of the most stable edges• = (nx1) vector most optimal profile locations
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12-Apr-2007 26
Overview
• Abstract• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments & Results• Conclusion
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12-Apr-2007 27
Experiments & Results
• Developed using ITK and VXL C++ open source libraries
• Subset of 10 high resolution MRI brain images from pediatric study
• 256x256x192 resolution
• Inter-voxel spacing 1.0mm
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12-Apr-2007 28
Experiments & Results
• Left hippocampus PDM– 42 shape instances– 642 corresponded landmark points– Corresponded using MDL
• Left caudate nucleus PDM– 85 shape instances– 742 corresponded landmark points– Corresponded using SPHARM
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12-Apr-2007 29
Experiments & Results
• Each PDM mean shape was manually initialized using Insight-SNAP
• Convergence was achieved when either the global transform variables or mahalanobis distance between ASM shape and PDM mean shape were at an minimum.
• Convergence was typically achieved between 5 to 7 ASM iterations using +/- 4 (k=9) profile locations along each landmark points normal vector
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12-Apr-2007 30
Experiments & Results
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12-Apr-2007 31
Experiments & Results
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12-Apr-2007 32
Experiments & Results
• ASM segmented performance was compared against Atlas-based method
• Performance was evaluated using the following measures:– Pearson correlation coefficient: volumetric
correlation– Dice coefficient: volumetric overlap
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12-Apr-2007 33
Experiments & Results
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12-Apr-2007 34
Overview
• Abstract• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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12-Apr-2007 35
Conclusion
• Presented two new strategies to address limitations of current ASM.– Robust edge detection to reduce likelihood of
spurious edge– Pro-active solution ensure ASM approximated shape
is defined within PDM shape space limits using unified cost function
• Additional research is required to address the sensitivity of the initial placement
• Implement fully-automatic method