Transcript Detection of Catheters in Noisy Fluoroscopy Images

Context-Enhanced Detection
of Electrophysiology Catheters
in Noisy Fluoroscopy Images
Erik Franken
Final presentation
Master’s project
Technische Universiteit Eindhoven
22 September 2004
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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Outline
1.
2.
3.
4.
5.
6.
Introduction
Local feature detection
Context enhancement
EP catheter extraction
Evaluation
Conclusions
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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1. Introduction
•
•
Application
Approach
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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1.1. Application: Cardiac Electrophysiology
Treatment of heart rhythm disorders
1.
2.
3.
Insertion of EP catheters
Recording of intracardiac electrograms
Ablation of problematic spot, or blocking undesired conduction path
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1.2. X-ray guidance
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1.3. Project goal: finding the EP catheters

• Restrict to spatial context
• Focus on noise robustness
• No initial seed position
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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1.4. Algorithm steps
A
B
C
A. Detect local image features (ridges, blobs)
B. Enhance local feature information
C. The decision step: group image features to
object of interest
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2. Local feature detection
•
•
•
Background equalization
Ridge detection
Blob detection
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2.1. Background equalization
Using morphological closing operation

Original image

Background image
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Background
normalized image
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2.2. Ridge detection
Catheter is locally ridge-shaped. Profile function:
Class of filters
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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2.3. Ridge detection
We use steerable filters

Example
Ridgeness
Orientations

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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2.4. Blob detection
Based on second eigenvalue of the Hessian matrix

b(x)  max(2 (x),0)
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2.5. Local features are too unreliable
…in case of noisy images

Source image
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Local ridgeness
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2.6. The importance of spatial context
Local filter
Context filter
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3. Context enhancement
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•
•
Introduction to tensor voting
Steerable tensor voting
Repeated tensor voting
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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3.1. Tensor voting components
Tensor voting
• Input: local feature data encoded in tensor field
• Model: voting field
• Operation: tensor communication
• Output: context enhanced tensor field
…versus Political elections
• Input: people with the right to vote
• Model: electoral system
• Operation: collection of votes from the polling stations
• Output: the parliament (with the elected people)
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3.2. Encoding in tensor field
For each pixel position, we have a tensor
in which the local features are encoded.
Graphical representation:
1 - 2 = orientation certainty
2 = orientation uncertainty
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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3.3. Voting field
Is a model for the continuation of line structures
Most likely
Least likely
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
V(x,y)
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3.4. Tensor communication
Voting field is used to let tensors vote for each other.
V
V
V
(x’,y’)
(x’,y’)
(x’,y’)
(x,y)
(x,y)
(x,y)
(x’,y’) = sender and (x,y) = recipient
 Amplification of smooth and elongated structures
 Filling of gaps in structures
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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3.5. Rotation of the voting field
Tensor field rotation:
where
By choosing an appropriate voting field, tensor voting
can be written in a steerable form
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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3.6. Steerable tensor voting scheme
• Using steerability, tensor voting boils down to (e.g.)
,
with

• Consists of complex-valued convolutions
• More efficient
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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3.7. Example - input


Source image
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Local ridgeness
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3.8. Example - result
|U2|
U2(x,y)=
*
+
*


+
*
+
*
+
*
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Context enhanced ridgeness
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3.9. Repeated tensor voting
Tensor voting  thinning  tensor voting
Result after first step
Result after second step

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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4. Catheter extraction
•
•
Overview
Step by step explanation on an example
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4.1. Overview
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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4.2. Example image
Source image
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Background equalized image
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4.3. Result of tensor voting (used as input)
Context enhanced ridgeness
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Blobness
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4.4. Extraction of paths
Local ridge maxima
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Extracted most salient paths
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4.5. Extraction of catheter tips
Electrode candidates
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Extracted catheter tips
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4.6. Extension of catheter tips
Selection of the best extension candidate for each tip.
Result:
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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5. Evaluation
•
•
Evaluation questions
Evaluation results
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5.1. Quantitative evaluation – questions
• Is there an added value of the tensor voting step?
?
• What is the robustness to noise?
• How feasible is extraction of tip, tip + additional
segment, and entire EP catheter in clinical images?
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5.2. Quantitative evaluation – clinical images
Low noise
High noise
%
100
%tip
80
TV
TV
%tip ext
60
No TV
%entire
No TV
40
20
ncatheters =103
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Low noise
High noise
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6. Conclusions and recommendations
•
•
Conclusions
Recommendations
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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6.1. Conclusions
Application:
• Tensor voting makes EP catheter extraction more
robust
• Detection of tip quite successful, detection of entire
catheters still error-prone
• Algorithms still far too slow
Context Enhancement methodology:
• Derived an efficient scheme for tensor voting
• Context enhancement methods will be useful for a lot
of other (medical) image analysis problems
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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6.2. Recommendations
Application:
• The use of temporal information
• Parameter optimization with larger test set
• More efficient implementation
Context Enhancement methodology:
• Include curvature
• Improve voting field
• Improve communication scheme
• Vote with other |m|-components
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Acknowledgements
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•
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Prof. Paul van den Bosch, prof. Bart ter Haar Romeny
Markus van Almsick, Peter Rongen
Other colleagues at TU/e
Other colleagues at PMS
Family
Friends
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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Questions
TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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