Neural computing for quantitative analysis of human bone trabecular structures in synchrotron radiation X-Ray μCT images

Prevention and treatment of osteoporosis in elderly patients is critical and important since this disease became a major public health problem. It is well known the fact that osteoporotic fractures may occur as a result of a combination of the degeneration of trabecular structures and low bone mass. Therefore, the quantitative analysis of human bone trabecular architecture might be useful for treatment and diagnosis of this disease. synchrotron radiation X-Ray micro-Computed Tomography (μCT) enables magnified images with a high space resolution that allows detailed analysis of the trabecular structure. In the quantitative analysis of medical images of human bone, it is necessary to use filters and binarization, nevertheless these techniques may cause loss of information. This paper describes the alternative application of neural computing (artificial neural networks) to the pixel classification in order perform the quantitative analysis of human bone trabecular structure in synchrotron radiation μCT images obtained at the Synchrotron Radiation for Medical Physics (SYRMEP) beam line of the ELETTRA Laboratory at Trieste, Italy. Results demonstrate that, despite the complexity of the trabecular architecture, the ANNs have considerable success in the recognition of bone pixels for the quantitative analysis and that its use is compatible to the characteristics of Synchrotron Radiation images.