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Segmentation & Modeling PowerPoint PPT Presentation


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Segmentation & Modeling. Images. Segmented Images. Models. Segmentation. Process of identifying structure in 2D & 3D images Output may be labeled pixels edge map set of contours. Approaches. Pixel-based Thresholding Region growing Edge/Boundary based Contours/boundary surface

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Segmentation & Modeling

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Segmentation modeling l.jpg

Segmentation & Modeling

Images

Segmented

Images

Models


Segmentation l.jpg

Segmentation

  • Process of identifying structure in 2D & 3D images

  • Output may be

    • labeled pixels

    • edge map

    • set of contours


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Approaches

  • Pixel-based

    • Thresholding

    • Region growing

  • Edge/Boundary based

    • Contours/boundary surface

    • Deformable warping

    • Deformable registration to atlases


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Thresholding


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Thresholding


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Thresholding


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Thresholding


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Region Growing


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Region Growing


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Region Growing


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Region Growing


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Region Growing


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Deformable Surfaces


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Deformable Surfaces


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Deformable Surfaces


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Deformable Surfaces


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Deformable Surfaces


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Deformable Surfaces


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Modeling

  • Representation of anatomical structures

  • Models can be

    • Images

    • Labeled images

    • Boundary representations


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Source: CIS p 73 (Lavallee image)

Surface Representations

  • Implicit Representations

  • Explicit Representations

    • Polyhedra

    • Interpolated patches

    • Spline surfaces

    • ...


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Polyhedral Boundary Reps

  • Common in computer graphics

  • Many data structures.

    • Winged edge

    • Connected triangles

    • etc.


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Source: C. Cutting, CIS Book


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Winged Edge

Pccwe

  • Baumgart 1974

  • Basic data structures

    • winged edge (topology)

    • vertex (geometry)

    • face (surfaces)

  • Key properties

    • constant element size

    • topological consistency

Pcwe

PVT

Nface

Pface

NVT

Nccwe

Ncwe


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Connected Triangles

  • Basic data structures

    • Triangle (topology, surfaces)

    • Vertex (geometry)

  • Properties

    • Constant size elements

    • Topological consistency

Vc

Na

Nb

Vb

Va

Nc


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2D-based Methods


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2D-based methods

  • Treat 3D volume as a stack of slices

  • Outline

    • Find contours in each 2D slice

    • Connect contours to create tiled surfaces


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3D-based methods

  • Segment image into labeled voxels

  • Define surface and connectivity structure

  • Can treat boundary element between voxels as a face or a vertex

v1

v2

Bndry

v1

v2


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Block form methods

  • “Cuberille”-type methods

  • Treat voxels as little cubes

  • May produce self-intersecting volumes

  • E.g., Herman, Udupa


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Ref: Udupa , CIS Book, p47


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Beveled form methods

  • “Marching cubes” type

  • Voxels viewed as 3D grid points

  • Vertices are points on line between adjacent grid points

  • E.g. Lorensen&Cline, Baker, Kalvin, many others


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Beveled form basic approach

  • Segment the 3D volume

  • Scan 3D volume to process “8-cells” sequentially

  • Use labels of 8 cells as index in (256 element) lookup table to determine where surfaces pass thru cell

  • Connect up topology

  • Use various methods to resolve ambiguities

Source: Kalvin survey


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Marching Cubes

  • Lorensen & Kline

  • Probably best known

  • Used symmetries to reduce number of cases to consider from 256 to 15

  • BUT there is an ambiguity


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Wyvill, McPheters, Wyvill

Step 1: determine edges on each face of 8 cube

Step 2: Connect the edges up to make surfaces


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Ambiguities

  • Arise when alternate corners of a 4-face have different labels

  • Ways to resolve:

    • supersampling

    • look at adjacencent cells

    • tetrahedral tessallation


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Tetrahedral Tessalation

  • Many Authors

  • Divide each 8-cube into tetrahedra

  • Connect tetrahedra

  • No ambiguities


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Alligator Algorithm

  • Phase 1: Initial Construction

  • Phase 2: Adaptive Merging


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Source: C. Cutting, CIS Book


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