Ultrasound Transducer Quality Control and Performance Evaluation Using Image Metrics.

This paper aims to two main goals, first goal is to achieve the characterization of quality control of ultrasound scanners based on the potential image metrics. On the other hand, the most effective goal is how to classify ultrasound scanners based on image metrics to evaluate performance of ultrasound transducer. The authors utilize the metrics to give information about the spatial arrangement of the gray levels in the specific interest region. The execution of ultrasound images metric based on a set of 19 metrics (i.e. contrast, gradient and Laplacian). This set reflects quality control of ultrasound scanners. The wok of this paper based on the best 6 metrics from 19 metrics which extracted from linear discriminative analysis (LDA). The classification methods used for minimum numbers of metrics are fused using support vector machine (SVM) and the highest classification method is back propagation neural network (BPNN) classifiers to get the main target of paper. Finally, the results show that objective performance evaluation of ultrasound transducer accuracy was 100% by using back propagation neural network classifier.