Applications are invited from self-funded students. Prospective applicants should hold, or be about to obtain, a minimum upper second class honours degree (or equivalent) in a related area/subject. For information on how to apply for this project, please contact the primary supervisor in the first instance. Applications should be submitted through the Faculty of Biology, Medicine and Health Doctoral Academy website (www.bmh.manchester.ac.uk/study/research/apply/).
C. Lindner, P.A. Bromiley, M.C. Ionita and T.F. Cootes. Robust and Accurate Shape Model Matching using Random Forest Regression Voting. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(9) p. 1862-1874, 2015.
P.A. Bromiley, J. Adams and T.F. Cootes. Localisation of Vertebrae on DXA Images using Constrained Local Models with Random Forest Regression Voting. Lecture Notes in Computational Vision and Biomechanics 20, p. 159-171, 2015.
P.A. Bromiley, E.P. Kariki, J.E. Adams and T.F. Cootes. Fully Automatic Localisation of Vertebrae in CT images using Random Forest Regression Voting. Lecture Notes in Computer Science 10182. p. 51-63, 2017.
P.A. Bromiley, E.P. Kariki, J.E. Adams and T.F. Cootes. Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling. Lecture Notes in Computer Science 10734, p. 133-147, 2018.
J. Staal, E. Kariki, R. Hyatt, M.K. Javaid, E. Russell, T. O'Neill, K. Poole, D. Chappell, R. Rajak. Multi-site opportunistic diagnosis of vertebral fragility fractures in computed tomography scans. Osteoporosis International 29(S2) p. S646, 2018.
FTE Category A staff submitted: 44.86
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