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 causalautoregressive(AR) andautoregressive 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 asacceptablefor clinical film reading.

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

This paper presents a segmentation based linear predictive coding (SLPC) method for multispectral images. Given a set of multispectral images, the SLPC method first segments it into statistically distinct regions. It then finds a suitable linear prediction model for each region. Finally, it 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 SLPC 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. This method has been applied to magnetic resonance head images with three spectral bands (one T1 weighted and two T2 weighted, 256/spl times/256/spl times/12 bits/image). Images compressed by a factor of more than 22 have been regarded as indistinguishable from the originals, by several radiologists.>

In this paper, we introduce a semi-adaptive vector quantization (SAVQ) method, which is a combination of the traditional VQ scheme using a fixed code book and the locally adaptive VQ (LAVQ) method which dynamically constructs a code book according to the input data stream. The code book in SAVQ consists of two parts: a fixed part that is designed based on certain training signals as in VQ, and an adaptive part that it updated based on the input vectors to be compressed. The proposed method is more effective than VQ and LAVQ for semi-stationary signals that have patterns common over different images as well as features specific to a particular image. Such is the case with medical images, which have similar tissue characteristics over different images, as well as with local variations that are patient and pathology dependent. The SAVQ as well as VQ and LAVQ methods have been applied to multispectral magnetic resonance brain images. The SAVQ has achieved higher compression ratios than the VQ and LAVQ methods over a wide range of reproduction quality, with more significant improvement in the mid to high quality range. Furthermore, under the same quality criterion, SAVQ requires a much smaller code book than VQ, making the former less time and memory demanding. Readings by neuroradiologists have suggested that images produced by SAVQ at compression ratios up to 40 (for MRI data with 3 or 4 images/set, 256 X 256 pixels/image, and 16 bits/pixel) are acceptable for primary reading.