Multiple Features Extraction and Fusion for Ultrasound Dynamic Images Classification

Abstract

Ultrasound examination is of great significance in the clinical diagnosis of diseases. Processing and analyzing ultrasound images through artificial intelligence technology and providing assistance in decision-making has been a hot topic of research for several years. However, since most medical images exist in the form of pictures, the current processing methods for ultrasound images basically continue to adopt the technical achievements related to static medical image processing not considering the characteristics reflected by the dynamically changing ultrasound images thus resulting in a missed diagnosis of diseases. To this end, this paper proposes an innovative multi-feature extraction and fusion method for ultrasound dynamic image classification which first extracts various types of underlying features such as texture, edge, and shape of salient targets in medical images that apply to dynamic images. Then, the feature frequency-inverse image frequency (FF-IIF) multi-feature fusion algorithm is used to generate an adaptive combined feature classification. In the experiments, the effects of the proposed algorithm are verified for three ultrasound examination items respectively. The experimental results show that the features extracted by the multi-feature fusion algorithm using FF-IIF still maintain a certain degree of fault tolerance and stability under the dynamic changes of ultrasound probe position and orientation. The computation time of the algorithm is moderate and perfectly adapted to the real-time examination of ultrasound medicine.