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 active appearance model and metacarpal radiogrammetry(Institute of Electrical and Electronics Engineers Inc., 2017) Sam, M.; Areeckal, A.S.; Sumam David, S.Osteoporosis is a condition of fragile bone with an increased susceptibility to fracture. Since the gold standard method used for the diagnosis of osteoporosis, Dual X-ray Absorptiometry (DXA), is expensive and not widely available in low economies, there is a need for low cost approaches to detect bone loss in people. A new automated radiogrammetric method for early diagnosis of osteoporosis from a single hand radiograph is proposed. In this technique, the third metacarpal bone is segmented from hand X-ray images using Active Appearance Models (AAM). Points of interest acquired from the segmented bone are used to take radiogrammetric measurements, from which bone indices are calculated. Data used in this work was acquired from 138 subjects in two hospitals in India. Significant radiogrammetric features were selected using statistical analysis. The bone indices are observed to be significantly correlated with Bone Mineral Density (BMD) of the lumbar spine measured using DXA. Different classification models were trained using the significant features. The results obtained are promising and can be used as a cost effective diagnostic tool for early detection of osteoporosis. © 2017 IEEE.Item Cortical volumetry using 3D reconstruction of metacarpal bone from multi-view images(Institute of Electrical and Electronics Engineers Inc., 2018) Jayakar, A.D.; Sambath, G.; Areeckal, A.S.; Sumam David, S.D.Osteoporosis is a disease caused by decrease in bone density, which makes the bone more susceptible to fractures. The currently used techniques to diagnose osteoporosis such as Dual X-ray Absorptiometry (DXA) and Quantitative Computed Tomography (QCT) are expensive and not widely available. Computerized radiogrammetry is a low cost technique used for the detection of bone loss. But it gives an areal measurement of the cortical bone density. In this paper, we propose a novel low cost technique to measure cortical bone volume for the diagnosis of osteoporosis. The proposed method uses a 3D reconstruction of third metacarpal using three views of hand radiographs and a template model as prior. The projection contours of the template model are registered with the X-ray images and the point-pair correspondence obtained is used to deform the template model. The shaft of the reconstructed bone is used for measuring the cortical volume. The proposed 3D reconstruction method is evaluated by comparison to a ground truth model and manually segmented X-ray images. The cortical volumetric measurements obtained are statistically analyzed for correlation with DXA measurement. The results obtained show that cortical volumetry using the proposed 3D reconstruction method can be developed into a low cost technique for the diagnosis of osteoporosis. © 2018 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.Item Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data(Elsevier Ltd, 2018) Areeckal, A.S.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis. © 2018 Elsevier Ltd