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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
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3 Introduction
What Is Segmentation ?
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4 Introduction
What Is Segmentation ?
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5 Introduction
What Is Segmentation ?
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6 Introduction
Applications Medical Imaging(v)
Object Detection
Recognition Tasks
Traffic Control Systems
Video Surveillance
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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
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8 Introduction
Vessel Segmentation Classification Pattern Recognition Techniques
Model-based
Tracking-based
Artificial Intelligence-based
Neural Network-based
Miscellaneous Tube-like Object Detection
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9 Introduction
Pattern Recognition Techniques Automatic Detection
Classification
Features
Disadvantage Be Difficult to Deal with Edge Noises and Branch Vessels.
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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
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11
Results
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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
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13 Introduction
Model-based Deformable Models
Parametric Models
Template Matching
Generalized Cylinders
Disadvantage Be Hard to Set Model Parameters and Affect the Computational
Cost
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14
Based on Model-based Classification Steps
Initialize Location and Contour
Local Morphology Fitting(LMF) Growing
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Related Papers -Local Morphology Fitting Active Contour for Automatic
Vascular Segmentation
15
Results
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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
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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
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18
Based on Tracking-based Classification Steps
Automatic Identification of Start Points
Tracking Based on Bayesian
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Related Papers -A Retinal Vessel Tracking Method
Based On Bayesian Theory
19
Results
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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
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21 Conclusion
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22 Conclusion
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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=EOCqzA2luy0
https://www.youtube.com/watch?v=lwMuJX480jo
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24
The EndThank you for listening
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