Random Walker Framework for Sensor-Based Echocardiography Fusion

Abstract

Image fusion techniques in 3-D echocardiography attempt to improve the field-of-view by combining multiple 3-D ultrasound (3-DUS) volumes. Echocardiography fusion techniques are mostly based on either image registration or sensor tracking. Compared to registration techniques, sensor tracking approaches are image independent and do not need any spatial overlap between the images. Once the images are spatially aligned the pixel intensities in the overlapping regions are determined using fusion algorithms such as average fusion (AVG) and maximum fusion (MAX). However, averaging generally results in reduced contrast while maximizing results in amplification of noise artifacts in the fused image. Wavelet fusion (WAV) overcomes these issues by selectively enhancing the low-frequency components in the image, but this could result in pixelation artifacts. We propose a new method for image fusion based on a generalized random walker framework (GRW) using ultrasound confidence maps. The maps are based on: 1) focal properties of the transducer and 2) second order image features. The fusion technique was validated on image pairs sampled from 3-DUS volumes acquired from six healthy volunteers. All the images were spatially aligned using optical tracking, and the fusion algorithm was used to determine the pixel intensity in the overlapping region. Comparisons based on quantitative measures showed statistically significant improvements for GRW (p < 0:01) when compared to AVG, MAX, and WAV for contrast-to-noise ratio: 0.85 ± 0.03, signal-to-noise ratio: 7.42 ± 1.98, Wang–Bovik metric (Q0): 0.80 ± 0.15. The Piella metric (Q1): 0.82 ± 0.01 also gave higher values for GRW, but the difference was not statistically significant. Upon visual inspection, the GRW fusion had the lowest amount of stitching and pixelation artifacts. The fusion technique proposed could help in improving the diagnostic accuracy and clinical acceptance of 3-D echocardiography.