Common Carotid Artery Ultrasound Image Enhancement Based on Fuzzy Set Theory and Phase Asymmetry Metric

Carotid ultrasound images can be used for computer-aided diagnosis of atherosclerosis. However, the low contrast and blurred edges in ultrasound images can reduce the accuracy of the diagnosis. In this study, an image enhancement algorithm is proposed for common carotid artery (CCA) ultrasound images. First, the CCA ultrasound images are normalized. Next, the normalized images are fuzzified by using the S-function. Then, the fuzzified images are enhanced based on non-linear transform. After enhancement, the images are transferred from the fuzzy domain to the spatial domain by using the inverse function. Finally, an edge enhancement method based on the phase asymmetry (PAS) metric is proposed for CCA ultrasound images. The final results indicate that the proposed method has outstanding performance as compared with other advanced methods.