Osteoporotic vertebral fractures (VFs) are under-diagnosed, creating an opportunity for computer-aided, opportunistic fracture identification in clinical images. VF diagnosis and grading in clinical practice involves comparisons of vertebral body heights. However, machine vision systems can provide a high-resolution segmentation of the vertebrae and fully characterise their shape and appearance, potentially allowing improved diagnostic accuracy. We compare approaches based on vertebral heights to shape/appearance modelling combined with k-nearest neighbours and random forest (RF) classifiers, on both dual-energy X-ray absorptiometry images and computed tomography image volumes. We demonstrate that the combination of RF classifiers and appearance modelling, which is novel in this application, results in a significant (up to 60% reduction in false positive rate at 80% sensitivity) improvement in diagnostic accuracy.
CITATION STYLE
Bromiley, P. A., Kariki, E. P., Adams, J. E., & Cootes, T. F. (2018). Classification of osteoporotic vertebral fractures using shape and appearance modelling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10734 LNCS, pp. 133–147). Springer Verlag. https://doi.org/10.1007/978-3-319-74113-0_12
Mendeley helps you to discover research relevant for your work.