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CSCE790T Medical Image Processing. University of South Carolina Department of Computer Science. 3D Active Shape Models Integrating Robust Edge Identification and Statistical Shape Models. Overview. Introduction Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function - PowerPoint PPT Presentation
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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
12-Apr-2007 2
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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
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.
12-Apr-2007 5
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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)
12-Apr-2007 7
Motivation
http://www.emedicine.com/radio/topic443.htm#target2
12-Apr-2007 8
Motivation
http://www.sci.uidaho.edu/med532/basal.htm
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
12-Apr-2007 10
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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
12-Apr-2007 12
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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
12-Apr-2007 14
Robust Edge Detection
Example sagittal plane edges for hippocampus
Image slice
Gradient magnitude slice
12-Apr-2007 15
Robust Edge Detection
Example coronal plane edges for hippocampus
Image slice
Gradient magnitude slice
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
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
12-Apr-2007 20
Robust Edge Detection
• Propose an new neighborhood solution• Spatially consistent profile location (i
)• Reduces the likelihood of an false edge
12-Apr-2007 21
Overview
• Introduction• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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
iiii
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12-Apr-2007 23
Unified Cost Function
• Steps in shape deformation where ASM shape not within PDM shape space limits
12-Apr-2007 24
Unified Cost Function
• Proposed solution implemented by an unified cost function
• Pro-active solution• Efficiently solved as an quadratic programming
problem
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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*
α T
• 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
12-Apr-2007 26
Overview
• Abstract• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments & Results• Conclusion
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
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
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
12-Apr-2007 30
Experiments & Results
12-Apr-2007 31
Experiments & Results
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
12-Apr-2007 33
Experiments & Results
12-Apr-2007 34
Overview
• Abstract• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments / Results• Conclusion
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