Multispectral code excited linear prediction coding and its application in magnetic resonance images

• Published in: IEEE transactions on image processing 1997-11, Vol.6 (11), p.1555-1566
• Main Authors: Jian-Hong Hu, Yao Wang, Cahill, P.T.
• Format: Article
• Language: English
• Subjects: Adaptation model; Applied sciences; Autoregressive processes; Biological and medical sciences; Cardiovascular system; Discrete cosine transforms; Discrete wavelet transforms; Exact sciences and technology; Image coding; Image processing; Image segmentation; Information, signal and communications theory; Investigative techniques, diagnostic techniques (general aspects); Magnetic resonance imaging; Mathematical models; Medical imaging; Medical sciences; Multispectral imaging; Pixel; Predictive models; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Regression analysis; Robustness; Signal analysis; Signal processing; Telecommunications and information theory; Trees (mathematics); Vector quantization; Wavelet transforms
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.641415

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, 256/spl times/256 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.