Faculty Publications
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Item Fully automated radiogrammetric measurement of third metacarpal bone from hand radiograph(Institute of Electrical and Electronics Engineers Inc., 2016) Areeckal, A.S.; Sumam David, S.; Kocher, M.; Jayasheelan, N.; Kamath, J.Osteoporosis is a disease caused by reduction of bone mass, bone strength and deterioration of bone structure. The gold standard method for diagnosis of osteoporosis is measurement of bone mineral density (BMD) using Dual X-ray Absorptiometry (DXA). However, DXA is expensive and not widely available in developing countries. An alternative cost-effective method for measurement of bone loss and strength is metacarpal radiogrammetry, by which geometric measurements of cortical bone of the metacarpal bone are measured. In this paper, we propose a fully automated method for segmentation of third metacarpal bone from hand radiograph and radiogrammetric measurements using mathematical morphology. Cortical width and thickness are measured from the endosteal and periosteal edges of the metacarpal bone using which bone indices which help in diagnosis of osteoporosis can be computed. The proposed segmentation method was tested on 157 hand X-ray images. A success rate of 94.9% is obtained for automatic detection of third metacarpal bone. Evaluation of cortical measurements of 3 calibrated images is done by comparing the results with ground truth. The mean accuracy error obtained was 0.02cm and 0.04cm for cortical width and medullary width, respectively. © 2016 IEEE.Item Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population(Springer London, 2018) Areeckal, A.S.; Jayasheelan, N.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.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. © 2017, International Osteoporosis Foundation and National Osteoporosis Foundation.