Ultrasound Artifacts Imaging Recognition by Hebbian-Trained Hopfield Networks

Abstract

Ultrasound imaging is a cornerstone of non-invasive diagnostics due to its real-time capability, safety, and low cost. However, image quality is often degraded by lingering vibrations of piezoelectric transducers after excitation, leading to artifacts. Hopfield Neural Networks (HNNs) have emerged as powerful tools for associative memory and pattern recognition, but their performance can be limited by dynamical instabilities, convergence to spurious states, and interference among overlapping patterns. This study investigates the principles and practical significance of HNNs in memory-based computational modeling. We examine limitations such as false minimum, memory capacity constraints, and instability, which can compromise accurate associative recall. Hebbian learning is applied to construct weight matrices that optimize memory storage, enhance fidelity of stored patterns, and reduce spurious attractors. Results show that properly tuned Hebbian-trained HNNs improve pattern stability and retrieval accuracy, especially in high-noise or memory-dense scenarios. enhancing structural consistency in both imaging and pattern analysis. Integrating Hebbian-trained HNNs with morphological filtering demonstrates superior performance in pattern recognition, retrieval reliability, and robustness under noisy or complex conditions. These findings highlight the potential of combining biologically inspired neural networks with image-processing techniques to enhance ultrasound image quality and computational modeling efficiency.