Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis
Current assessment of osteoarthritis (OA) is primary based on visual grading of joint space narrowing and osteophytes present on radiographs. The approach is observer-dependent, not sensitive enough for the detection of the early stages of OA and time consuming. A promising solution is through fract...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/24847 |
| _version_ | 1848751542549610496 |
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| author | Stachowiak, Gwidon Wolski, Marcin Woloszynski, Tomasz Podsiadlo, Pawel |
| author_facet | Stachowiak, Gwidon Wolski, Marcin Woloszynski, Tomasz Podsiadlo, Pawel |
| author_sort | Stachowiak, Gwidon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Current assessment of osteoarthritis (OA) is primary based on visual grading of joint space narrowing and osteophytes present on radiographs. The approach is observer-dependent, not sensitive enough for the detection of the early stages of OA and time consuming. A promising solution is through fractal analysis of trabecular bone (TB) textures on radiographs. The goal is to develop an automated decision support system for the detection and prediction of OA based on TB texture regions selected on knee and hand radiographs. In this review, we describe our progress towards this development which was conducted in five stages, i.e., (i) development of automated methods for the selection of TB texture regions on knee and hand radiographs (ii), development of fractal signature methods for TB texture analysis, (iii) applications of the methods in the analysis of x-ray images of knees and hands, (iv) development of TB texture classification system, and (v) development of ReadMyXray website for knee x-ray analysis. The results achieved so far are encouraging and it is hoped, that once the system is fully developed and evaluated, it will be used to aid medical practitioners in the decision-making, i.e., in designing OA preventative measures, treatments and monitoring the OA progression. |
| first_indexed | 2025-11-14T07:54:23Z |
| format | Journal Article |
| id | curtin-20.500.11937-24847 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:54:23Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-248472019-09-24T08:07:24Z Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis Stachowiak, Gwidon Wolski, Marcin Woloszynski, Tomasz Podsiadlo, Pawel Current assessment of osteoarthritis (OA) is primary based on visual grading of joint space narrowing and osteophytes present on radiographs. The approach is observer-dependent, not sensitive enough for the detection of the early stages of OA and time consuming. A promising solution is through fractal analysis of trabecular bone (TB) textures on radiographs. The goal is to develop an automated decision support system for the detection and prediction of OA based on TB texture regions selected on knee and hand radiographs. In this review, we describe our progress towards this development which was conducted in five stages, i.e., (i) development of automated methods for the selection of TB texture regions on knee and hand radiographs (ii), development of fractal signature methods for TB texture analysis, (iii) applications of the methods in the analysis of x-ray images of knees and hands, (iv) development of TB texture classification system, and (v) development of ReadMyXray website for knee x-ray analysis. The results achieved so far are encouraging and it is hoped, that once the system is fully developed and evaluated, it will be used to aid medical practitioners in the decision-making, i.e., in designing OA preventative measures, treatments and monitoring the OA progression. 2016 Journal Article http://hdl.handle.net/20.500.11937/24847 10.1016/j.bsbt.2016.11.004 http://creativecommons.org/licenses/by-nc-nd/4.0 Elsevier fulltext |
| spellingShingle | Stachowiak, Gwidon Wolski, Marcin Woloszynski, Tomasz Podsiadlo, Pawel Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis |
| title | Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis |
| title_full | Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis |
| title_fullStr | Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis |
| title_full_unstemmed | Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis |
| title_short | Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis |
| title_sort | detection and prediction of osteoarthritis in knee and hand joints based on the x-ray image analysis |
| url | http://hdl.handle.net/20.500.11937/24847 |