Download presentation
Presentation is loading. Please wait.
Published bySteven Reed Modified over 9 years ago
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
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.