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

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Presentation on theme: "Introduction to Related Papers of Vessel Segmentation Methods Advisor : Ku-Yaw Chang Student : Wei-Lu Lin 2015/1/7."— Presentation transcript:

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

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

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

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

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

6 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

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 7

8 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

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

10 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

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

12 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

13 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

14  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

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

16 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

17 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

18  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

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

20 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

21 Conclusion 2015/1/7 21

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

23 References  [1http://www.di.ens.fr/~laptev/objectdetection.html  [2] http://bit.ly/1BNIAem  https://www.youtube.com/watch?v=ceIddPk78yA&list= PLz8K9D6W9hwa1I-LZhmyC7ux1fXi1VSzu&index=8 https://www.youtube.com/watch?v=ceIddPk78yA&list= PLz8K9D6W9hwa1I-LZhmyC7ux1fXi1VSzu&index=8  https://www.youtube.com/watch?v=EOCqzA2l uy0 https://www.youtube.com/watch?v=EOCqzA2l uy0  https://www.youtube.com/watch?v=lwMuJX480 jo https://www.youtube.com/watch?v=lwMuJX480 jo 2015/1/7 23

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


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