The purpose of our study is to apply a neural-network technique to detect changes of trabecular patterns due to osteoporosis on skeletal radiographs. Regions of interest (ROIs) (64x64pixels,6.4x6.4mm2) near the center of the vertebral body were extracted from digitized bone radiographs. After the background-trend correction was performed by a curve fitting technique, texture measures were calculated, such as standard deviations (total, horizontal, and vertical directions), maximum and minimum pixel values, and conditional entropy, which were used as input data to the input layer of the neural network. The network was trained with a back-propagation algorithm to discriminate textural differences between the normal and the diseased cases (2 outputs), or between the normal, early stage,1st stage,2nd stage and 3rd stage (5 outputs). A data base of 44 bone radiographs was collected. Half of the cases was used as the training set, and the other half as the testing set. The neural networks with 2 outputs and 5 outputs correctly classified about 86% and 66% of the cases at maximum, respectively. It was found to be most difficult to detect the early-stage image data; the correct recognitions of about 95% and 76% at maximum were obtained for 2 outputs and 5 outputs cases, respectively. Our results suggest that the neural-network analysis is useful to discriminate textural variations of trabecular patterns and is effective for computer-aided diagnosis system of osteoporosis.

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Neural-network approach to the restoration of blurred and/or noisy radiographic images is presented. Basic study was performed using a flower image, which was blurred by a Gaussian filter. A multi-layer, feed forward neural network with a back-propagation algorithm was designed to restore the degraded images. The effects of the network parameters, such as the number of neurons in each layer, on the restoration capability were experimentally investigated. The digital image restoration system based on the neural network was successful to improve the image quality with optimum network parameters. The structure consisting of 5×5 input neurons,10 hidden neurons, and one output neuron was most effective in our study. The network applied to the chest radiographs also showed a good performance. It was found that additional noise decreases the restoring capability. The preliminary results demonstrate the potential of an artificial neural network to restore the degraded radiographs.

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