Hybrid Techniques with Support Vector Machine for Improving Artifact Ultrasound Images

Abstract

     The most common artifacts in ultrasound (US) imaging are reverberation and comet-tail. These are multiple reflection echoing the interface that causing them, and result in ghost echoes in the ultrasound image. A method to reduce these unwanted artifacts using a Otsu thresholding to find region of interest (reflection echoes) and output applied to median filter to remove noise. The developed method significantly reduced the magnitude of the reverberation and comet-tail artifacts. Support Vector Machine (SVM) algorithm is most suitable for hyperplane differentiate. For that, we use image enhancement, extraction of feature, region of interest, Otsu thresholding, and finally classification image datasets to normal or abnormal image. Because of the machine’s training for both types of images, the machine can now predict whether a new image is an abnormal image or a normal image. As a result, it reduced medical work for many checkups and other things. Our proposed method shows the correct classification result by more than 89%.