TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 22-09-2004 1 Context-Enhanced Detection of...

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


Outline1. Introduction

2. Local feature detection

3. Context enhancement

4. EP catheter extraction

5. Evaluation

6. Conclusions


1. Introduction• Application

• Approach


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


1.2. X-ray guidance


1.3. Project goal: finding the EP catheters

• Restrict to spatial context

• Focus on noise robustness

• No initial seed position


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


2. Local feature detection• Background equalization

• Ridge detection

• Blob detection


2.1. Background equalization

Using morphological closing operation

Original image Background image Background normalized image


2.2. Ridge detection

Catheter is locally ridge-shaped. Profile function:

Class of filters


2.3. Ridge detection

Orientations

RidgenessExample

We use steerable filters


2.4. Blob detection

Based on second eigenvalue of the Hessian matrix

)0),(max()( 2 xx b


2.5. Local features are too unreliable

Source image Local ridgeness

…in case of noisy images


2.6. The importance of spatial context

Local filter

Context filter


3. Context enhancement• Introduction to tensor voting

• Steerable tensor voting

• Repeated tensor voting


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)


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:


3.3. Voting field

Is a model for the continuation of line structures

Most likely

Least likely

V(x,y)


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


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


3.6. Steerable tensor voting scheme

• Using steerability, tensor voting boils down to (e.g.)

• Consists of complex-valued convolutions

• More efficient

with

,


3.7. Example - input

Source image Local ridgeness


3.8. Example - result

Context enhanced ridgeness

*

*

*

*

*

+

+

+

+

U2(x,y)=|U2|


3.9. Repeated tensor voting

Tensor voting thinning tensor voting

Result after first step Result after second step


4. Catheter extraction• Overview

• Step by step explanation on an example


4.1. Overview


4.2. Example image

Source image Background equalized image


Context enhanced ridgeness Blobness

4.3. Result of tensor voting (used as input)


Local ridge maxima Extracted most salient paths

4.4. Extraction of paths


Electrode candidates Extracted catheter tips

4.5. Extraction of catheter tips


4.6. Extension of catheter tips

Selection of the best extension candidate for each tip.

Result:


5. Evaluation• Evaluation questions

• Evaluation results


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?


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


6. Conclusions and recommendations• Conclusions

• Recommendations


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


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


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


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