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Our objective is to contribute a reliable platform for segmenting and classifying vertebral fractures. We present a method for the automatic segmentation of the Vertebral Bodies (VB) from 2D sagittal images of the spine followed by classifying if the VB is normal or fractured. In our work, we use Dual energy X-ray Absorptiometry (DXA) images which is a standard technique to quantify Bone Mineral Density (BMD) of vertebrae that best indicates osteoporotic vertebral fracture. A certain level of preprocessing is required as the DXA images appear noisy. After image binarization, morphological operations are applied to close the missing links and to remove small invalid regions. The Region of Interest (ROI) is portioned by extricating fine contour of vertebral endplates. The count of Connected Components (CC) indicates the presence of inferior and superior endplates of the adjacent vertebrae. A complete VB is segmented by choosing those two CCs with the largest centroid distance. Principal Component Analysis (PCA) inspired angle estimation for each CC is used to classify if the segmented VB is fractured or not. Our approach has been demonstrated to be feasible both in terms of efficiency and robustness by being tested on 527 vertebrae on DXA images at a classification accuracy of 93.93% compared to a gold standard of manual annotation by expert radiologists.

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