Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population

A. S. Areeckal, N. Jayasheelan, J. Kamath, S. Zawadynski, M. Kocher, S. David S

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Summary: We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. Introduction: We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Methods: Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws’ masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. Results: In our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%. Conclusion: An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.

Original languageEnglish
Pages (from-to)665-673
Number of pages9
JournalOsteoporosis International
Volume29
Issue number3
DOIs
Publication statusPublished - 01-03-2018

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Wrist
Osteoporosis
Early Diagnosis
Hand
Bone and Bones
Costs and Cost Analysis
Metacarpal Bones
Population
Photon Absorptiometry
Masks
India
Healthy Volunteers
Health

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism

Cite this

Areeckal, A. S. ; Jayasheelan, N. ; Kamath, J. ; Zawadynski, S. ; Kocher, M. ; David S, S. / Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population. In: Osteoporosis International. 2018 ; Vol. 29, No. 3. pp. 665-673.
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abstract = "Summary: We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. Introduction: We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Methods: Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws’ masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. Results: In our pilot study, the proposed segmentation method shows 89.9 and 93.5{\%} accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3{\%} and test accuracy of 88.5{\%}. Conclusion: An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.",
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Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population. / Areeckal, A. S.; Jayasheelan, N.; Kamath, J.; Zawadynski, S.; Kocher, M.; David S, S.

In: Osteoporosis International, Vol. 29, No. 3, 01.03.2018, p. 665-673.

Research output: Contribution to journalArticle

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AU - Kocher, M.

AU - David S, S.

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AB - Summary: We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. Introduction: We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Methods: Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws’ masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. Results: In our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%. Conclusion: An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.

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