Computer-aided osteoporosis detection from DXA imaging. (May 2019)

• Record Type: Journal Article
• Authors: Hussain, Dildar; Han, Seung-Moo
• Is Part Of: Computer methods and programs in biomedicine. Volume 173(2019)
• Page Start: 87
• Page End: 107
• Publication Date: 2019-05
• Subjects: Osteoporosis -- DXA -- Image segmentation -- Select region of interest (ROI) -- Bone density -- Contours processing; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.03.011
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: In view of the limitations of population-based studies tools and standardization, researchers have turned their attention to automations in DXA imaging to bring consistency in BMD measurements. Automation with reliable accuracy improves the utilization of health care devices. Automatic detection of osteoporosis disease allows anyone to use a DXA system and produce high-quality data when compared to domain experts. Automatic diagnosing tools in healthcare domain reduce workload. In DXA imaging system segmentation and BMD measurement accuracy will be improved with machine learning based algorithms. Abstract: Background and objective: Osteoporosis is a skeletal disease caused by a high rate of bone tissue loss, and it is a major cause of bone fracture. In contemporary society, osteoporosis is more common than cancer and stroke and results in a higher rate of morbidity and mortality in the human population. Osteoporosis can conclusively be diagnosed with dual energy X-ray absorptiometry (DXA). In this study, we propose a computer-aided osteoporosis detection (CAOD) technique that automatically measures bone mineral density (BMD) and generates an osteoporosis report from a DXA scan. Methods: The CAOD model denoise and segments DXA images using a non-local mean filter, Machine learning pixel label random forest respectively, and locates regions of interest with higher accuracy. Pixel label random forest classifies a pixel either bone or soft tissue; then contours are extracted from binary image to locate regions of interest and calculate BMD from bone and soft tissues pixels. Mean standard deviation and correlation coefficients statistical analysis were used to evaluate the consistency and accuracy of BMD measurements. Results: During a consistency test of BMD measurements using three consecutive scans from Computerized Imaging Reference Systems' Bona Fide Phantom (CIRS-BFP) for the spine, the CAOD model showed an averaged standard deviation of 0.0029 while the standard deviation from manual measurements on the same data set by three different individuals was recorded as 0.1199. During another correlation study of BMD measurements evaluating real human scan images by the CAOD model versus manual measurement, the model scored a correlation coefficient of R 2 = 0.9901 while the CIRS-BFP study scored a correlation coefficient of R 2 = 0.9709. Conclusions: The CAOD model increases the preciseness and accuracy of BMD measurements. This CAOD method will help clinicians, untrained DXA operators, and researchers (medical scientists, doctors, and bone researchers) use the DXA system with reliable accuracy and overcome workload challenges. It will also improve osteoporosis diagnosis from DXA systems and increase system performance and value.