Optimizing feedforward artificial neural network architecture
Engineering Applications of Artificial Intelligence
Analysis of trabecular bone structure using Fourier transforms and neural networks
IEEE Transactions on Information Technology in Biomedicine
A decision support system for the prediction of the trabecular fracture zone
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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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驴=驴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驴