Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative

Manual selection of finger trabecular bone texture regions on hand X-ray images is time-consuming, tedious, and observer-dependent. Therefore, we developed an automated method for the region selection. The method selects square trabecular bone regions of interest above and below the second to fifth...

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Main Authors: Wolski, M., Englund, M., Stachowiak, Gwidon, Podsiadlo, P.
Format: Journal Article
Published: Sage Publications 2016
Online Access:http://purl.org/au-research/grants/arc/DE130100771
http://hdl.handle.net/20.500.11937/45559
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author Wolski, M.
Englund, M.
Stachowiak, Gwidon
Podsiadlo, P.
author_facet Wolski, M.
Englund, M.
Stachowiak, Gwidon
Podsiadlo, P.
author_sort Wolski, M.
building Curtin Institutional Repository
collection Online Access
description Manual selection of finger trabecular bone texture regions on hand X-ray images is time-consuming, tedious, and observer-dependent. Therefore, we developed an automated method for the region selection. The method selects square trabecular bone regions of interest above and below the second to fifth distal and proximal interphalangeal joints. Two regions are selected per joint (16 regions per hand). The method consists of four integral parts: (1) segmentation of a radiograph into hand and background, (2) identification of finger regions, (3) localization of center points of heads of distal phalanges and the distal interphalangeal, proximal interphalangeal, and metacarpophalangeal joints, and (4) placement of the regions of interest under and above the distal and proximal interphalangeal joints. A gold standard was constructed from regions selected by two observers on 40 hand X-ray images taken from Osteoarthritis Initiative cohort. Datasets of 520 images were generated from the 40 images to study the effects of hand and finger positioning. The accuracy in regions selection and the agreement in calculating five directional fractal parameters were evaluated against the gold standard. The accuracy, agreement, and effects of hand and finger positioning were measured using similarity index (0 for no overlap and 1 for entire overlap) and interclass correlation coefficient as appropriate. A high accuracy in selecting regions (similarity index ≥ 0.79) and a good agreement in fractal parameters (interclass correlation coefficient ≥ 0.58) were achieved. Hand and finger positioning did not affect considerably the region selection (similarity index ≥ 0.70). These results indicate that the method developed selects bone regions on hand X-ray images with accuracy sufficient for fractal analyses of bone texture.
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spelling curtin-20.500.11937-455592022-09-01T02:24:21Z Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative Wolski, M. Englund, M. Stachowiak, Gwidon Podsiadlo, P. Manual selection of finger trabecular bone texture regions on hand X-ray images is time-consuming, tedious, and observer-dependent. Therefore, we developed an automated method for the region selection. The method selects square trabecular bone regions of interest above and below the second to fifth distal and proximal interphalangeal joints. Two regions are selected per joint (16 regions per hand). The method consists of four integral parts: (1) segmentation of a radiograph into hand and background, (2) identification of finger regions, (3) localization of center points of heads of distal phalanges and the distal interphalangeal, proximal interphalangeal, and metacarpophalangeal joints, and (4) placement of the regions of interest under and above the distal and proximal interphalangeal joints. A gold standard was constructed from regions selected by two observers on 40 hand X-ray images taken from Osteoarthritis Initiative cohort. Datasets of 520 images were generated from the 40 images to study the effects of hand and finger positioning. The accuracy in regions selection and the agreement in calculating five directional fractal parameters were evaluated against the gold standard. The accuracy, agreement, and effects of hand and finger positioning were measured using similarity index (0 for no overlap and 1 for entire overlap) and interclass correlation coefficient as appropriate. A high accuracy in selecting regions (similarity index ≥ 0.79) and a good agreement in fractal parameters (interclass correlation coefficient ≥ 0.58) were achieved. Hand and finger positioning did not affect considerably the region selection (similarity index ≥ 0.70). These results indicate that the method developed selects bone regions on hand X-ray images with accuracy sufficient for fractal analyses of bone texture. 2016 Journal Article http://hdl.handle.net/20.500.11937/45559 10.1177/0954411916676219 http://purl.org/au-research/grants/arc/DE130100771 Sage Publications restricted
spellingShingle Wolski, M.
Englund, M.
Stachowiak, Gwidon
Podsiadlo, P.
Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative
title Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative
title_full Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative
title_fullStr Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative
title_full_unstemmed Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative
title_short Automated selection of bone texture regions on hand radiographs: Data from the Osteoarthritis Initiative
title_sort automated selection of bone texture regions on hand radiographs: data from the osteoarthritis initiative
url http://purl.org/au-research/grants/arc/DE130100771
http://hdl.handle.net/20.500.11937/45559