Automated selection of trabecular bone regions in knee radiographs.
Osteoarthritic (OA) changes in knee joints can be assessed by analyzing the structure of trabecular bone (TB) in the tibia. This analysis is performed on TB regions selected manually by a human operator on x-ray images. Manual selection is time-consuming, tedious, and expensive. Even if a radiologist expert or highly trained person is available to select regions, high inter- and intraobserver variabilities are still possible. A fully automated image segmentation method was, therefore, developed to select the bone regions for numerical analyses of changes in bone structures. The newly developed method consists of image preprocessing, delineation of cortical bone plates (active shape model), and location of regions of interest (ROI). The method was trained on an independent set of 40 x-ray images. Automatically selected regions were compared to the "gold standard" that contains ROIs selected manually by a radiologist expert on 132 x-ray images. All images were acquired from subjects locked in a standardized standing position using a radiography rig. The size of each ROI is 12.8 x 12.8 mm. The automated method results showed a good agreement with the gold standard [similarity index (SI) = 0.83 (medial) and 0.81 (lateral) and the offset =[-1.78, 1.27]x[-0.65,0.26] mm (medial) and [-2.15, 1.59]x[-0.58, 0.52] mm (lateral)]. Bland and Altman plots were constructed for fractal signatures, and changes of fractal dimensions (FD) to region offsets calculated between the gold standard and automatically selected regions were calculated. The plots showed a random scatter and the 95% confidence intervals were (-0.006, 0.008) and (-0.001, 0.011). The changes of FDs to region offsets were less than 0.035. Previous studies showed that differences in FDs between non-OA and OA bone regions were greater than 0.05. ROIs were also selected by a second radiologist and then evaluated. Results indicated that the newly developed method could replace a human operator and produces bone regions with an accuracy that is sufficient for fractal analyses of bone texture.
Podsiadlo
P
P
Tribology Laboratory, School of Mechanical Engineering, The University of Western Australia, Crawley 6009, Western Australia. pawel@mech.uwa.edu.au
Wolski
M
M
Stachowiak
G W
GW
D001331:Automation; D001842:Bone and Bones / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D017709:Fractals; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007717:Knee / Q000000981:diagnostic imaging*; D007719:Knee Joint / Q000000981:diagnostic imaging* / Q000473:pathology; D015233:Models, Statistical; D010003:Osteoarthritis / Q000000981:diagnostic imaging*; D020370:Osteoarthritis, Knee / Q000000981:diagnostic imaging* / Q000473:pathology; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012542:Scattering, Radiation; D012984:Software; D014965:X-Rays