Purpose. This study was aimed to validate the performance of a novel image compression method using a neural network to achieve a lossless compression. The encoding consists of the following blocks: a prediction block
a residual data calculation block
a transformation and quantization block
an organization and modification block
and an entropy encoding block. The predicted image is divided into four macro-blocks using the original image for teaching
and then redivided into sixteen sub-blocks. The predicted image is compared to the original image to create the residual image. The spatial and frequency data of the residual image are compared and transformed. Materials and methods. Chest radiography, computed tomography (CT), magnetic resonance imaging, positron emission tomography, radioisotope mammography, ultrasonography, and digital subtraction angiography images were compressed using the AIC lossless compression method
and the compression rates were calculated. Results. The compression rates were around 15:1 for chest radiography and mammography, 12:1 for CT, and around 6:1 for other images. This method thus enables greater lossless compression than the conventional methods. Conclusion. This novel method should improve the efficiency of handling of the increasing volume of medical imaging data. © 2008 Japan Radiological Society.