Region of Interest Extraction using K-Means and Edge Detection for DEXA Images

Authors

  • Abdulkareem Z. Mohammed Computer Science, Informatics institute for postgraduate studies, Baghdad,10089, IRAQ https://orcid.org/0000-0002-0376-5100
  • Loay E. George University of Information Technology & Communication (UoITc), Baghdad, 10089, IRAQ

DOI:

https://doi.org/10.55145/ajest.2023.02.02.006

Keywords:

Edge Detection, K-mean, Region of interest, Dual energy x-ray absorptiometry, DEXA, preprocessing

Abstract

 Region of interest in the world of medical images, there is a region in each image that contains
unique features that distinguish each image from the other or distinguish one group of the image from another
group. This paper proposes a new method for extracting the region of interest for DEXA images via the K-means
and edge detection. firstly, the noise is reduced by the mean filter then segment the image into two clusters by kmeans, followed by edge detection to identify object boundaries and erosion operation to clarify boundaries and get
the correct coordinates of ROI. The results show that the accuracy of the proposed system is 99%. where 174
images cropped correctly out of 176. the dataset used in this work is 'Osteoporosis DEXA Scans Images of Spine
from Pakistan'.

Downloads

Published

2023-02-26

How to Cite

Z. Mohammed, A., & George, L. E. (2023). Region of Interest Extraction using K-Means and Edge Detection for DEXA Images. Al-Salam Journal for Engineering and Technology, 2(2), 48–53. https://doi.org/10.55145/ajest.2023.02.02.006

Issue

Vol. 2 No. 2 (2023)

Section

Articles

License

Copyright (c) 2023 Abdulkareem Z. Mohammed, Loay E. George

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.