Assessment and classification of mechanical strength components of human femur trabecular bone using texture analysis and neural network.

In this work the mechanical strength components of human femur trabecular bone are analyzed and classified using planar radiographic images and neural network. The mechanical strength regions such as Primary Compressive, Primary Tensile, Secondary Tensile and Ward Triangle in femur trabecular bone images (N = 100) are delineated by semi-automatic image processing procedure. First and higher order texture parameters and parameters such as apparent mineralization and total area associated with the strength regions are derived for normal and abnormal images. The statistically derived significant parameters corresponding to the primary strength regions are fed to the neural network for training and validation. The classifications are carried out using feed forward network that is trained with standard back propagation algorithm. Results demonstrate that the apparent mineralization of normal samples is always high as (71%) compared to abnormal samples (64%). Entropy shows a high value (7.3) for normal samples and variation between the mean intensity and apparent mineralization for the primary strength zone is statistically significant (p < 0.0005). The classified outputs are validated by sensitivity and specificity measurements and are found to be 66.66% and 80% respectively. Further it appears that it is possible to differentiate normal and abnormal samples from the conventional radiographic images. As trabecular architecture in the human femur is an important factor contributing to bone strength, the procedure adopted here could be a useful supplement to the clinical observations for bone loss and fracture risk.