Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{153117,
        author = {Dr. Shubhangi D.C and B. Ayesha},
        title = {BMD Calculation for Osteoporosis detection from DXA Scan Images Using K-Means Clustering Bone Segmentation},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {5},
        pages = {571-576},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153117},
        abstract = {A common systemic skeletal disorder called Osteoporosis leads to decrease bone strength and increase vulnerability to osteofragility fracture. A measure termed Bone Mineral Density is used to detect the disease (BMD). Several image processing and machine learning algorithms are used to estimate BMD in both X-ray and DXA pictures. This methodology comprises segmentation algorithms like k-means clustering and mean-shift algorithms, as well as a comparison of algorithm accuracy. In addition, a futuristic mathematical approach is presented to accurately detect the osteoporosis state by measuring the values of T–score in DXA pictures with a new metric ‘S' derived from Bone Mineral Density(BMD) data.},
        keywords = {BMD(Bone Mineral Density), DXA, T-Score, X-ray},
        month = {},
        }