Early Diagnosis of Osteoporosis using Metacarpal Radiogrammetry and Texture Analysis

Abstract

Osteoporosis is a disease characterized by reduction in bone mass and micro-structure, leading to increased risk of fragility fractures. The gold standard technique used for diagnosis of osteoporosis is determination of Bone Mineral Density (BMD) using Dual Energy X-ray Absorptiometry (DXA). DXA is accurate and precise, however it has a high cost of scan and low availability in developing countries. The aim of the research work is to develop a low cost prescreening tool for early diagnosis of osteoporosis using cortical radiogrammetry and trabecular texture analysis of hand and wrist radiographs. An automatic method for segmentation of the third metacarpal bone shaft from hand and wrist radiographs is proposed using automatically detected anatomical landmarks, intensity profiles and marker-controlled watershed segmentation. From the outer and inner bone edges of the segmented third metacarpal bone shaft, cortical radiogrammetric features are extracted. The proposed method is validated on sample data of two ethnic groups: 138 Indian subjects and 65 Swiss subjects. The proposed segmentation method accurately detected the third metacarpal bone in 89% of Indian sample data and 78% of Swiss sample data. The proposed method shows better performance as compared with the state-of-the-art segmentation method, Active Appearance Model (AAM). A segmentation approach is proposed to automatically extract the distal radius for trabecular texture analysis. The proposed method extracted the distal radius region-ofinterest in 93.5% of Indian sample data and 83% of Swiss sample data. Texture analysis of the trabecular bone in distal radius is done. The extracted features are analyzed using independent sample t-test and Pearson correlation analysis. The cortical radiogrammetric features show high discrimination ability in the healthy and low bone mass groups of both Indian and Swiss sample data. The cortical and texture features are divided into different feature sets. Classifiers are trained on cortical features and statistical and structural texture features for Inviidian sample data and a linear regression model is estimated. Artificial Neural Network (ANN) classifier trained using holdout validation achieves test accuracy of 90.0%. kNearest Neighbor (KNN) using 10-fold cross validation achieves an accuracy of 81.7%. The linear regression model developed with the cortical and texture features achieves a significant correlation of 0.671 with DXA-BMD. Classifiers are also trained separately for Indian and Swiss sample population. ANN classifiers trained with significant cortical and statistical and structural texture features show test accuracy of 92.9% with Indian data and 90.9% with Swiss data. Weighted KNN using the same feature set shows test accuracy of 96.2% using holdout validation. A novel method to measure the cortical volume of the metacarpal bone shaft at a low cost using three dimensional reconstruction from hand X-ray images in three views (Postero-Anterior, 450 and 1350 oblique views) is proposed. The Computed Tomography scan of one subject is used to create a template model, from which subject-specific models of other subjects are reconstructed. The 3D reconstruction of the bone is done iteratively by registration of projection and X-ray contours using Iterative Closest Point and Self-Organizing Map, and deformation of the template model using Laplacian surface deformation. The outer and inner bone walls of the metacarpal are modeled separately and the third metacarpal bone shaft is extracted from which cortical volume is measured. The projections of the 3D reconstructed models are compared with manually segmented X-ray images and the mean percentage error in Combined Cortical Thickness (CCT) is 11.18%. In summary, a low cost prescreening tool for early diagnosis of osteoporosis using cortical radiogrammetry and texture analysis is proposed and validated using sample data of Indian and Swiss population. A low cost method to measure cortical volume of third metacarpal bone shaft using multi-view hand radiographs is also proposed. This work is done in collaboration with Kasturba Medical College Hospital, Mangalore, India and University Hospital of Geneva, Switzerland.