TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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, Outline 1.Introduction 2.Local feature detection 3.Context enhancement 4.EP catheter extraction 5.Evaluation 6.Conclusions

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Introduction Application Approach

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, X-ray guidance

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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, Algorithm steps ABC A.Detect local image features (ridges, blobs) B.Enhance local feature information C.The decision step: group image features to object of interest

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Local feature detection Background equalization Ridge detection Blob detection

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Background equalization Using morphological closing operation Original imageBackground imageBackground normalized image 

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Ridge detection Catheter is locally ridge-shaped. Profile function: Class of filters

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Ridge detection Orientations  Ridgeness Example We use steerable filters 

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Blob detection Based on second eigenvalue of the Hessian matrix 

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Local features are too unreliable Source imageLocal ridgeness …in case of noisy images 

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, The importance of spatial context Local filter Context filter

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Context enhancement Introduction to tensor voting Steerable tensor voting Repeated tensor voting

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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)

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Encoding in tensor field = orientation certainty 2 = orientation uncertainty For each pixel position, we have a tensor in which the local features are encoded. Graphical representation:

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Voting field Is a model for the continuation of line structures Most likely Least likely V(x,y)

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Tensor communication Voting field is used to let tensors vote for each other.  Amplification of smooth and elongated structures  Filling of gaps in structures V V V (x’,y’) (x,y)(x,y)(x,y)(x,y) (x,y)(x,y) (x’,y’) = sender and (x,y) = recipient

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Rotation of the voting field Tensor field rotation: By choosing an appropriate voting field, tensor voting can be written in a steerable form where

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Steerable tensor voting scheme Using steerability, tensor voting boils down to (e.g.) Consists of complex-valued convolutions More efficient with ,

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Example - input Source imageLocal ridgeness  

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Example - result  Context enhanced ridgeness * * * * * U 2 (x,y)= |U 2 | 

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Repeated tensor voting Tensor voting  thinning  tensor voting Result after first stepResult after second step 

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Catheter extraction Overview Step by step explanation on an example

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Overview

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Example image Source imageBackground equalized image

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Context enhanced ridgenessBlobness 4.3. Result of tensor voting (used as input)

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Local ridge maximaExtracted most salient paths 4.4. Extraction of paths

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Electrode candidatesExtracted catheter tips 4.5. Extraction of catheter tips

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Extension of catheter tips Selection of the best extension candidate for each tip. Result:

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Evaluation Evaluation questions Evaluation results

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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?

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Quantitative evaluation – clinical images Low noiseHigh noise n catheters =103

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Conclusions and recommendations Conclusions Recommendations

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, 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

TU/e, BMIA & PMS, X-Ray Predevelopment, Erik Franken, Questions