1TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Context-Enhanced Detection of Electrophysiology Cathetersin Noisy Fluoroscopy Images
Erik Franken
Final presentation
Master’s project
Technische Universiteit Eindhoven
22 September 2004
2TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Outline1. Introduction
2. Local feature detection
3. Context enhancement
4. EP catheter extraction
5. Evaluation
6. Conclusions
3TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
1. Introduction• Application
• Approach
4TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
1.1. Application: Cardiac Electrophysiology
Treatment of heart rhythm disorders
1. Insertion of EP catheters
2. Recording of intracardiac electrograms
3. Ablation of problematic spot, or blocking undesired conduction path
5TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
1.2. X-ray guidance
6TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
1.3. Project goal: finding the EP catheters
• Restrict to spatial context
• Focus on noise robustness
• No initial seed position
7TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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
8TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
2. Local feature detection• Background equalization
• Ridge detection
• Blob detection
9TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
2.1. Background equalization
Using morphological closing operation
Original image Background image Background normalized image
10TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
2.2. Ridge detection
Catheter is locally ridge-shaped. Profile function:
Class of filters
11TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
2.3. Ridge detection
Orientations
RidgenessExample
We use steerable filters
12TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
2.4. Blob detection
Based on second eigenvalue of the Hessian matrix
)0),(max()( 2 xx b
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2.5. Local features are too unreliable
Source image Local ridgeness
…in case of noisy images
14TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
2.6. The importance of spatial context
Local filter
Context filter
15TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3. Context enhancement• Introduction to tensor voting
• Steerable tensor voting
• Repeated tensor voting
16TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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)
17TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3.2. Encoding in tensor field
1 - 2 = orientation certainty
2 = orientation uncertainty
For each pixel position, we have a tensor
in which the local features are encoded.
Graphical representation:
18TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3.3. Voting field
Is a model for the continuation of line structures
Most likely
Least likely
V(x,y)
19TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3.4. Tensor communication
Voting field is used to let tensors vote for each other.
Amplification of smooth and elongated structures
Filling of gaps in structures
VV V
(x’,y’)(x’,y’)
(x’,y’)
(x,y) (x,y) (x,y)
(x’,y’) = sender and (x,y) = recipient
20TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3.5. Rotation of the voting field
Tensor field rotation:
By choosing an appropriate voting field, tensor voting can be written in a steerable form
where
21TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3.6. Steerable tensor voting scheme
• Using steerability, tensor voting boils down to (e.g.)
• Consists of complex-valued convolutions
• More efficient
with
,
22TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3.7. Example - input
Source image Local ridgeness
23TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
3.8. Example - result
Context enhanced ridgeness
*
*
*
*
*
+
+
+
+
U2(x,y)=|U2|
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3.9. Repeated tensor voting
Tensor voting thinning tensor voting
Result after first step Result after second step
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4. Catheter extraction• Overview
• Step by step explanation on an example
26TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
4.1. Overview
27TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
4.2. Example image
Source image Background equalized image
28TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Context enhanced ridgeness Blobness
4.3. Result of tensor voting (used as input)
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Local ridge maxima Extracted most salient paths
4.4. Extraction of paths
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Electrode candidates Extracted catheter tips
4.5. Extraction of catheter tips
31TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
4.6. Extension of catheter tips
Selection of the best extension candidate for each tip.
Result:
32TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
5. Evaluation• Evaluation questions
• Evaluation results
33TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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?
34TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
20
40
60
80
100%
Low noise High noise
TV
No TV
TV
No TV%entire
%tipext
%tip
5.2. Quantitative evaluation – clinical images
Low noise High noise
ncatheters =103
35TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
6. Conclusions and recommendations• Conclusions
• Recommendations
36TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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
6.1. Conclusions
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
37TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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
38TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
Acknowledgements
• 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
39TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004
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