Segmented Adaptive DPCM for Lossy Compression of Multispectral MR Images

This paper reports a multispectral segmented differential pulse coded modulation (MSDPCM) method for well registered multispectral magnetic resonance (MR) images. Given a set of multispectral MR images, the MSDPCM method first segments it into statistically distinct regions which by and large correspond to different tissue classes. It then finds a suitable linear prediction model (LPM) for each class. The LPMs used here are the well known causal autoregressive (AR) and autoregressive moving average (ARMA) models. Finally, the MSDPCM method quantizes the prediction error in each class using a vector quantizer. The original image set is described by the segmentation map, the model parameters for each class, and the quantized prediction errors. The MSDPCM method can produce very high compression gains, because the specification of the segmentation map and model parameters requires significantly fewer bits than that for the original intensity values. The MSDPCM method using the backward adaptive ARMA model has been applied to head MR images with three spectral bands (one T1 weighted and two T2 weighted, 256 × 256 × 12 bits/image). In an informal validation, the compressed images have been evaluated against the originals by three neuroradiologists. Images compressed by an average factor of more than 23 have been regarded as acceptable for clinical film reading.