Deep Learning based Enhanced Adaptive Despeckling for Ultrasound Images

Abstract

Considering the inadequacies observed in traditional medical ultrasound image de-speckling techniques, this research presents an innovative solution: a feedforward convolutional neural network (CNN) model coupled with an adaptive multi-exposure fusion framework. The study initiates by curating a specialized ultrasound image training dataset.It then proposes a multi-exposure fusion framework with adaptive enhancement factors to improve image quality for more efficient feature extraction. The proposed method trains a speckle model through the neural network and achieves the extraction of a de-speckled image. Experimental results unequivocally demonstrate the method's unparalleled effectiveness in speckle noise reduction within medical ultrasound images while concurrently preserving intricate image details, thus exemplifying its potential in clinical applications.