Medical Ultrasound Image Quality Enhancement and Regions Segmentation

Authors

  • Ayat Ali Al-jaburi Computer Eng. Department, College of Engineering, University of Baghdad, Baghdad, Iraq
  • Ahlam Hanoon AL-sudani Computer Eng. Department, College of Engineering, University of Baghdad, Baghdad, Iraq

DOI:

https://doi.org/10.24996/ijs.2022.63.10.35

Keywords:

US, Speckle noise, Artifact, Region Growing, Segmentation

Abstract

     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.

Downloads

Download data is not yet available.

Downloads

Published

2022-10-30

How to Cite

Al-jaburi, A. A., & AL-sudani, A. H. (2022). Medical Ultrasound Image Quality Enhancement and Regions Segmentation. Iraqi Journal of Science, 63(10), 4518–4533. https://doi.org/10.24996/ijs.2022.63.10.35

Issue

Section

Computer Science

Most read articles by the same author(s)