7 research outputs found
Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography
Ultrasound elastography estimates the mechanical properties of the tissue
from two Radio-Frequency (RF) frames collected before and after tissue
deformation due to an external or internal force. This work focuses on strain
imaging in quasi-static elastography, where the tissue undergoes slow
deformations and strain images are estimated as a surrogate for elasticity
modulus. The quality of the strain image depends heavily on the underlying
deformation, and even the best strain estimation algorithms cannot estimate a
good strain image if the underlying deformation is not suitable. Herein, we
introduce a new method for tracking the RF frames and selecting automatically
the best possible pair. We achieve this by decomposing the axial displacement
image into a linear combination of principal components (which are calculated
offline) multiplied by their corresponding weights. We then use the calculated
weights as the input feature vector to a multi-layer perceptron (MLP)
classifier. The output is a binary decision, either 1 which refers to good
frames, or 0 which refers to bad frames. Our MLP model is trained on in-vivo
dataset and tested on different datasets of both in-vivo and phantom data.
Results show that by using our technique, we would be able to achieve higher
quality strain images compared to the traditional methods of picking up pairs
that are 1, 2 or 3 frames apart. The training phase of our algorithm is
computationally expensive and takes few hours, but it is only done once. The
testing phase chooses the optimal pair of frames in only 1.9 ms