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%.
Medical Ultrasound (US) has many features that make it widely used in the world. These features are safety, availability and low cost. However, despite these features, the ultrasound suffers from problems. These problems are speckle noise and artifacts. In this paper, a new method is proposed to improve US images by removing speckle noise and reducing artifacts to enhance the contrast of the image. The proposed method involves algorithms for image preprocessing and segmentation. A median filter is used to smooth the image in the pre-processing. Additionally, to obtain best results, applying median filter with different kernel values. We take the better output of the median filter and feed it into the Gaussian filter, which then feeds the output of the Gaussian filter into histogram equalization to improve image visualization. The segmentation is done by thresholding and region growing segmentation. The value of threshold 128 was found to be better after we tested many values of thresholding. This value of thresholding combined with region growing gave accurate result segmentation of images. This paper demonstrates how image noise, artifacts and techniques were used effectively to improve image quality, and the analysis of performance of various techniques.
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