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.
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.
Authors: Kyoung Ho Lee; Hak Jong Lee; Jae Hyoung Kim; Heung Sik Kang; Kyung Won Lee; Helen Hong; Ho Jun Chin; Kyoo Seob Ha Journal: J Digit Imaging Date: 2005-09 Impact factor: 4.056
Authors: Ramesh Neelamani; Ricardo de Queiroz; Zhigang Fan; Sanjeeb Dash; Richard G Baraniuk Journal: IEEE Trans Image Process Date: 2006-06 Impact factor: 10.856
Authors: David Wu; Damian M Tan; Marilyn Baird; John DeCampo; Chris White; Hong Ren Wu Journal: IEEE Trans Med Imaging Date: 2006-03 Impact factor: 10.048