Development and evaluation of a novel lossless image compression method (AIC: artificial intelligence compression method) using neural networks as artificial intelligence.
OBJECTIVE
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.


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.


CONCLUSIONS
This novel method should improve the efficiency of handling of the increasing volume of medical imaging data.
Fukatsu
Hiroshi
H
Department of Radiology, Nagoya University Hospital, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Japan. fukatsu@med.nagoya-u.ac.jp
Naganawa
Shinji
S
Yumura
Shinnichiro
S
D044962:Data Compression / Q000379:methods*; D003952:Diagnostic Imaging; D006801:Humans; D016571:Neural Networks, Computer; D011873:Radiology Information Systems; D012984:Software