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

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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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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)

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Motivation

http://www.emedicine.com/radio/topic443.htm#target2

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Motivation

http://www.sci.uidaho.edu/med532/basal.htm

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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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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

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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

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Robust Edge Detection

Example sagittal plane edges for hippocampus

Image slice

Gradient magnitude slice

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Robust Edge Detection

Example coronal plane edges for hippocampus

Image slice

Gradient magnitude slice

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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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Robust Edge Detection

• Additionally, each landmark points normal vector is determined by the surface mesh

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Robust Edge Detection

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Robust Edge Detection

• Generally, edges detection along search paths are considered dangerous

• Subject to noise

• Spurious (false) edges

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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

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Unified Cost Function

• Steps in shape deformation where ASM shape not within PDM shape space limits

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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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Unified Cost Function

• The cost function can be viewed as,

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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

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Overview

• Abstract• Motivation• General ASM Algorithm• Robust Edge Detection• Unified Cost Function• Experiments & Results• Conclusion

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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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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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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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Experiments & Results

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Experiments & Results

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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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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