Lossless Compression of Medical Images Using a Dual Level DPCM with Context Adaptive Switching Neural Network Predictor

DOI

10.1080/18756891.2013.816059How to use a DOI?

Keywords

dual level DPCM (DL-DPCM), neural networks, lossless image compression, medical image

Abstract

A novel dual level differential pulse code modulation (DL-DPCM) is proposed for lossless compression of medical images. The DL-DPCM consists of a linear DPCM followed by a nonlinear DPCM namely, context adaptive switching neural network predictor (CAS-NNP). The CAS-NNP adaptively switches between three NN predictors based on the context texture of the predicted pixel in the image. Experiments on magnetic resonance (MR) images showed lower prediction error for the DL-DPCM compared to the GAP and the MED, which are used in benchmark algorithms CALIC and LOCO-I respectively. The overall improvement in data reduction after entropy coding the prediction error were 0.21 bpp (6.5%) compared to the CALIC and 0.40 bpp (11.7%) compared to the LOCO-I.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Emjee Puthooran
AU  - R S Anand
AU  - S Mukherjee
PY  - 2013
DA  - 2013/11/01
TI  - Lossless Compression of Medical Images Using a Dual Level DPCM with Context Adaptive Switching Neural Network Predictor
JO  - International Journal of Computational Intelligence Systems
SP  - 1082
EP  - 1093
VL  - 6
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.816059
DO  - 10.1080/18756891.2013.816059
ID  - Puthooran2013
ER  -