Symmetrybased Segmentation and Recognition - PowerPoint PPT Presentation

Title: Symmetrybased Segmentation and Recognition


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Symmetry-based Segmentation and Recognition
Thomas B. Sebastian
Brown University
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Summary

• Three main contributions of my dissertation
• Effective segmentation method for carpal bones
  in CT images
• Implements skeletal coupling between seeds
• Solves convergence problem of deformable models
• Generic curve matching algorithm with several
  applications
• Robust shape recognition algorithm for matching
  shock graphs
• Gives excellent recognition rates for indexing
  large shape databases

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Contents

• Segmentation using skeletally coupled deformable
  model
• Introduction
• Previous approaches
• Skeletal coupling
• Results
• Curve matching and its applications
• Shape recognition using shock graphs
• Indexing into large shape databases

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Medical application

• Study 3D kinematics of carpal bones
• Identify wrist injuries where radiographs are
  normal
• Quantify shape of bone to characterize disease
  progression, e.g., in Kienbock disease
• Compute curvature maps

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Challenges

• Weak and diffused edges

• Gaps in bone boundary

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Challenges (cont.)

• Texture in spongy bone

• Narrow inter-bone gaps

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Contents

• Segmentation using skeletally coupled deformable
  model
• Introduction
• Previous approaches
• Skeletal coupling
• Results
• Curve matching and its application
• Shape recognition using shock graphs
• Indexing into large shape databases

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Deformable models
Initialize multiple seeds, which grow under
image-dependent forces

• Drawbacks
• Fail to converge near weak edges
• Do not capture narrow inter-bone gaps

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Seeded region growing Biscoff PAMI

• Initializes seeds and grows them by annexing an
  adjacent pixel
• Only the "closest" pixel is added at each
  iteration
• Implements global competition among all seeds

• Drawbacks
• Leaks into small gaps as there are no geometric
  constraints

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Region Competition Zhu/Yuille, PAMI

• Initializes seeds and grows them using a
  combination of statistical and smoothing forces

• Implements local competition between seeds when
  they contact each other
• Allows for exchange of pixels between regions and
  recovery from errors

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Region Competition (cont.)

• Drawbacks
• Merges some adjacent bones
• Fails to capture low-contrast bones

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Contents

• Segmentation using skeletally coupled deformable
  model
• Introduction
• Previous approaches
• Skeletal coupling
• Results
• Curve matching and its application
• Shape recognition using shock graphs
• Indexing into large shape databases

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Skeletal coupled deformable model (SCDM)

• SCDM combines advantages of previous techniques
• Subpixel nature of deformable models
• Global competition of seeded region growing
• Local competition of region competition

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Skeletal coupling Overview
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Growth of regions

• In isolation, growth of regions depends on a
  local statistical force

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Local competition by skeletal coupling

• The inter-region skeleton is used to couple
  growing regions
• The regions compete for pixels in the middle

• When seeds come in contact, l1, same as region
  competition

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Long-range skeletal competition

• The inter-region skeleton is viewed as predicted
  boundary and the growth of regions is modulated
  by its suitability

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Total skeletally-coupled forces

• The total statistical force is a combination of
  local and long-range forces

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Contents

• Segmentation using skeletally coupled deformable
  model
• Introduction
• Previous approaches
• Skeletal coupling
• Results
• Curve matching and its application
• Shape recognition using shock graphs
• Indexing into large shape databases

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Convergence of SCDM

• SCDM solves the convergence problem of
  traditional deformable models

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SCDM segmentation results
Results are clinically meaningful
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3D model of carpal bones
3D model is created by stacking up 2D contours
3D model is useful in studying carpal kinematics,
creating computational atlases, etc.
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Carpal bone segmentation Comparison
SRG/RegComp
SCDM

• Rating by hand surgeons (RIH)
• SCDM 83
• SRG 14
• RegComp 3

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Contents

• Segmentation using skeletally coupled deformable
  model
• Introduction
• Previous approaches
• Skeletal coupling
• Results
• Curve matching and its applications
• Shape recognition using shock graphs
• Indexing into large shape databases

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Curve matching Sebastian et al, PAMI

• Goal is to find optimal alignment (pairing of
  points) and distance (deformation cost)

• Cost is measured by length and curvature
  differences of infinitesimal segments and summing
  them

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Curve matching Sebastian et al, PAMI

• Alignment is a pairing of points of C, C
• Treat curves C and C as the x and y-axes
• Alignment is then a curve in 2D space, called
  alignment curve
• Dynamic programming to find optimal alignment
  curve

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Prototype formation

• Average curve can be computed by averaging
  corresponding sub-segments

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Shape morphing

• Weighted averages can be used to generate morph
  sequence

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Handwritten character recognition

• 327 characters (34 categories)
• 98.5 recognition rate

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Gesture recognition
Intuitive matches
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Gesture recognition (cont.)
Outperforms other methods
Precision
Recall
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Contents

• Segmentation using skeletally coupled deformable
  model
• Introduction
• Previous approaches
• Skeletal coupling
• Results
• Curve matching and its applications
• Shape recognition using shock graphs
• Indexing into large shape databases

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Shock graph representation of shapes

• Shocks (or medial axis or skeleton) are locus of
  centers of maximal circles that are bitangent to
  shape boundary

Shape boundary
Shocks
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Distance between shapes

• Shape space is collection of all shapes
• Shape is a point
• Shape deformation sequence is a path

• Cost of optimal deformation sequence is distance
  from A to B

• There are infinitely many deformation paths
• Shape space has to be discretized

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Changes in shock graph topology

• Shock graph topology is unaltered for most shape
  deformations

• At transition shapes small changes lead to abrupt
  changes in the shock graph topology

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Partitioning of shape space

• Shape cell Collection of shapes with same shock
  graph topology

• Transition shapes form the boundary between shape
  cells

Shape cell 2
Shape cell 1
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Discretization of deformation paths

• Shape deformation bundle Collection of
  deformation paths passing through the same set of
  shape cells

• Represented by set of transition shapes it passes
  through

Dynamic programming is used to find optimal edit
sequence
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Matching results Sebastian et al, ICCV 01
Edit-distance algorithm gives intuitive results
Same colors indicate matching edges
gray-colored edges are pruned
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Robustness to transformations
Boundary noise
In optimal edit sequence noisy branches are
pruned
Articulation
Edit-distance is robust in presence of part-based
changes
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Robustness to transformations (cont.)
Viewpoint variation

• Deform edit handles smooth changes
• Splice and contract edits handle abrupt changes

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Robustness to transformations (cont.)
Partial occlusion
Edit-distance is robust to partial occlusion
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Indexing into shape databases
Edit-distance algorithm allows 100 shape
recognition between different shape categories
Results duplicated in two databases 99 shapes
and 216 shapes
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Indexing Results
It also allows nearly 100 recognition between
shapes in the same shape category
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Contents

• Segmentation using skeletally coupled deformable
  model
• Introduction
• Previous approaches
• Skeletal coupling
• Results
• Curve matching and its applications
• Shape recognition using shock graphs
• Indexing into large shape databases

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Indexing into large databases ECCV 02

• Indexing results are excellent using a large
  database (1032 shapes, 200 exemplars)
• Correct category is selected in top 1, 2 and 5
  matches with 78, 91, and 99 success rate
  respectively

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Number of exemplars

• Using fewer exemplars suggests a hierarchical
  representation
• 2-3 primary exemplars rule out 75 of categories
• Additional 2-3 auxiliary exemplars rule out
  another 50 of categories

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Acknowledgments

• Collaborators/Advisors
• Prof. Benjamin Kimia, Brown University
• Prof. Philip Klein, Brown University
• Dr. Joseph Crisco, RI Hospital

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Selected References

• SCDM segmentation
• Segmentation of carpal bones from CT images using
  SCDM MedIA02
• Segmentation of carpal bones using SCDM
  MICCAI98
• Curve matching
• On aligning curves PAMI02
• Alignment-based recognition of shape outlines
  IWVF01
• Constructing curve atlases MMBIA00
• Shock-graph matching
• Recognition of shapes by editing their shock
  graphs ICCV01
• Shape matching using edit distance An
  implementation SODA01
• Indexing into databases
• Shock-based indexing into large databases
  ECCV02
• Metric-based shape retrieval in large databases
  ICPR02
• Curves vs. skeletons for object recognition
  ICIP01