technische universiteit eindhoven /department of biomedical engineering segmentation techniques for...

technische universiteit eindhoven

/department of biomedical engineering

Segmentation Techniques for the Visible mouse

By Virjanand Panday

Supervisor: B.M. ter Haar Romeny

Date: 4 June 2003



The Visible Mouse Project

• Magnetic Resonance Lab

• 3D database of the mouse

• Develop non-invasive diagnostic routines for humans



Comparable to The Visible Human

• Fast overview of organs and possible anomalies

• For 3D rendering organs must first be detected from surrounding tissue



Example: Detection Kidney

• Weak edge

• Structures inside

• Strong edges surrounding



Definition of Problem

Investigate segmentation techniques for the visible mouse



Overview of Presentation

• Introduction

• Overview Segmentation Techniques

• Watershed Segmentation

•Active Contours Segmentation

• Levelset

• Snake

• Some Results

• Discussion



Classification Segmentation Techniques

Region Based:

• Homogeneities

• Region Growing

Edge Based

• Inhomogeneities

• LoG, Sobel operators

• Graph Searching or dynamic programming

Classification:

• Multiple values

• Clusters

•Minimum distance to means or artificial neural networks



Used Segmentation Techniques

• Watershed (uses region and edge based techniques)

• Active Contours (geometric reasoning)



Watershed: Theory

Image as landscape

Dam construction

Plaatje van landscape plaatje van sinus dam



Watershed: Nabla Vision



Active Contours

• Forces derived from the curve itself (Curvature, Size surface enclosed, etc.)

• Forces derived from image (Gradient, Texture, etc.)

• Other Forces (Balloon force)

Plaatje van random contour op muis beeld



Levelset: Evolution Function

Fnt

x

0))(( tx

n

Fn

F

t

x

00

F

F

t

x

xtdt

dtt

Plaatje van cone distance function



Levelset: Flow Function F

Constant: F= Curvature: F=-



Levelset: Stopping Terms

IGeg

Filmpje curve met stop term

Plaatje stopping term

IGc

0)( ce IGt



Snake: Evolution Equation

nIGs

sV

ss

sV

st

sV

)()()( 2

2

2



Comparing Results



Results Visualized in 3D

• Multiple slices



Discussion and Future Work

• Watershed needs postprocessing, but automatic

• Levelset requires strong edges (Stronger attraction towards edges), but addapts very well to topology of image

• Snake stays one contour, but spans gaps in edges well

• 3D looks smoother if methods applied directly on 3D: for levelset easy with 4D function, for snake a lot of bookkeeping of the surface points

• Better testing of correctness segmentation



Acknowledgements

• Bart ter Haar Romeny

• Aurelie Bugeau

• Markus van Almsick

• Anna Vilanova Bartroli

• Arnold Schilham

• Gustav Strijker and Willem Mulder

• Paul van den Bosch

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