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Proceedings Paper

Automated bone fracture detection
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Paper Abstract

Fractures of bone are a common affliction. In most developed countries the number of fractures associated with age-related bone loss is increasing rapidly. Each year many fractures are missed during x-ray diagnosis, resulting in ineffective patient management and expensive litigation. From both an orthopaedic and radiologic point of view, the fully automatic detection and classification of fractures in long-bones is an important but difficult problem. In this paper, a fully automated method of detecting fractures in the diaphysis of a long-bone is described. X-rays are very difficult to process automatically, so to extract the required information a non-linear anisotropic diffusion method, the Affine Morphological Scale Space, was implemented to smooth the image without losing information about the location of boundaries within the image. Next, an iterative peak detection algorithm is used to accurately locate the bone centreline and articular surfaces. A method based on orthogonal projections calculated from a modified Hough transform is used to automatically locate the long-bone diaphysis. At this point, our algorithm accurately localises the area of the fracture, and would allow further image registration if necessary. Finally, a gradient-based algorithm is used to detect fractures present in the region of interest. The magnitude and direction of the gradient are combined to produce a measure of the likelyhood of the presence of a fracture. A library of long-bone fracture images was created. Experimental tests performed on a series of x-ray images show that the method is capable of accurately segmenting the diaphysis from the epiphyses, and is also able to detect many mid-shaft fractures of long-bones.

Paper Details

Date Published: 29 April 2005
PDF: 12 pages
Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.594449
Show Author Affiliations
Martin Donnelley, Flinders Univ. (Australia)
Greg Knowles, Flinders Univ. (Australia)


Published in SPIE Proceedings Vol. 5747:
Medical Imaging 2005: Image Processing
J. Michael Fitzpatrick; Joseph M. Reinhardt, Editor(s)

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