Introduction to Related Papers of Vessel Segmentation Methods Advisor : Ku-Yaw Chang Student : Wei-Lu Lin 2015/1/7

Outline  Introduction  Related Papers  Conclusion 2015/1/7 2

Introduction  What Is Segmentation ? 2015/1/7 3

Introduction  What Is Segmentation ? 2015/1/7 4

Introduction  What Is Segmentation ? 2015/1/7 5

Introduction  Applications  Medical Imaging(v)  Object Detection  Recognition Tasks  Traffic Control Systems  Video Surveillance 2015/1/7 6 People Detection[1] License Plate Recognition[2] Vessel Segmentation

Introduction  Vessel Segmentation Classification  Pattern Recognition Techniques  Model-based  Tracking-based  Artificial Intelligence-based  Neural Network-based  Miscellaneous Tube-like Object Detection 2015/1/7 7

Introduction  Vessel Segmentation Classification  Pattern Recognition Techniques  Model-based  Tracking-based  Artificial Intelligence-based  Neural Network-based  Miscellaneous Tube-like Object Detection 2015/1/7 8

Introduction  Pattern Recognition Techniques  Automatic Detection  Classification  Features  Disadvantage  Be Difficult to Deal with Edge Noises and Branch Vessels. 2015/1/7 9

Related Papers – Adaptive Segmentation of Vessels from Coronary Angiograms Using Multi-scale Filtering  Based on Pattern Recognition Techniques Classification  Steps  Select Well-contrast Angiograms  Vessels Segmentation from the Well-contrast Angiograms  Using Multi-scale Hessian Matrix 2015/1/7 10

 Results 2015/1/7 11 Related Papers - Adaptive Segmentation of Vessels from Coronary Angiograms Using Multi-scale Filtering

Introduction  Vessel Segmentation Classification  Pattern Recognition Techniques  Model-based  Tracking-based  Artificial Intelligence-based  Neural Network-based  Miscellaneous Tube-like Object Detection 2015/1/7 12

Introduction  Model-based  Deformable Models  Parametric Models  Template Matching  Generalized Cylinders  Disadvantage  Be Hard to Set Model Parameters and Affect the Computational Cost 2015/1/7 13

 Based on Model-based Classification  Steps  Initialize Location and Contour  Local Morphology Fitting(LMF) Growing 2015/1/7 14 Related Papers - Local Morphology Fitting Active Contour for Automatic Vascular Segmentation

 Results 2015/1/7 15 Related Papers - Local Morphology Fitting Active Contour for Automatic Vascular Segmentation

Introduction  Vessel Segmentation Classification  Pattern Recognition Techniques  Model-based  Tracking-based  Artificial Intelligence-based  Neural Network-based  Miscellaneous Tube-like Object Detection 2015/1/7 16

Introduction  Tracking-based  Manual Start Points  Local Operators  Focus Known to Be a Vessel and Track It  Disadvantage  Cannot Effectively Track Vessels in Complex Background  Mostly Rely on the Manual Setting 2015/1/7 17

 Based on Tracking-based Classification  Steps  Automatic Identification of Start Points  Tracking Based on Bayesian 2015/1/7 18 Related Papers - A Retinal Vessel Tracking Method Based On Bayesian Theory

 Results 2015/1/7 19 Related Papers - A Retinal Vessel Tracking Method Based On Bayesian Theory other this paper

Conclusion  Segmentation Algorithms  A lot of methods  Future  Persuade more faster, more accurate and more automated  In My Opinion  Automation is not important  Interaction 2015/1/7 20

Conclusion 2015/1/7 21

Conclusion 2015/1/7 22 Contrast Image Image Contrast Image Image Contrast Image Image Contrast Image Image

References  [1http://  [2]  PLz8K9D6W9hwa1I-LZhmyC7ux1fXi1VSzu&index=8 PLz8K9D6W9hwa1I-LZhmyC7ux1fXi1VSzu&index=8  uy0 uy0  jo jo 2015/1/7 23

The End Thank you for listening 2015/1/7 24