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(Semi-)Automatic Detection of ECG Electrodes in MR and CT Images

(Semi-)Automatic Detection of ECG Electrodes in MR and CT Images
type:Student research project
tutor:

M.Sc. Steffen Schuler

person in charge:

Alper Gece

Motivation
Solving the the inverse problem of electrocardiography requires precise knowledge of ECG electrode positions. Manual segmentation is time-consuming and error-prone. The distinct shape of ECG electrodes and then proximity to the torso surface may allow for an automatic detection.

Preliminary work:
A template matching approach based on cross-correlating the image with a (rotated) 3D template of an electrode showed the following problems:
-Intensity inhomogeneities in MR images make it difficult to define a global threshold that separates electrodes from background in the correlation output, even when using zero-mean normalized cross-correlation.
-The template still needs to be optimized for maximum gain in SNR.

The project involves the following steps:
-Manually segment many electrodes and derive and optimal optimal template by averaging individual electrode segmentations.
-Find a robust way to normalize the cross-correlation or to define a threshold.
-Evaluate the use of other feature descriptors (for example the histogram of oriented gradients).
-Combine several feature descriptors to crate a stronger classifier.
-Make use of the regular spatial arrangement of electrodes within one electrode strip.