Abstract
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.
Highlights
Medical images provide anatomical and pathological details of the human body parts by noninvasive means
Better results are achieved by the dual level differential pulse code modulation (DL-differential pulse code modulation (DPCM)) as the context adaptive switching neural network predictor (CAS-NNP) is used, in which each neural network (NN) predictor is tuned for a particular area in the image
The dual level DPCM is realized by cascading the 2D-DPCM and the CAS-NNP
Summary
Medical images provide anatomical and pathological details of the human body parts by noninvasive means. Aiazzi et al.[15] proposed a fuzzy matching pursuits (FMPs) encoder, which consists of a space-varying linear-regression predictor obtained through fuzzy-logic techniques It achieved an average improvement of 0.108 bpp and 0.29 bpp over the CALIC and the JPEG-LS respectively, when tested on a set of 24 natural as well as medical images. The focus of the present work is to develop a lossless compression technique with lower decoder complexity and higher coding efficiency for medical images such as MR. In this method a dual level differential pulse code modulator (DL-DPCM) has been proposed to obtain higher prediction accuracy.
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