Open Access

Multilevel Wavelet Feature Statistics for Efficient Retrieval, Transmission, and Display of Medical Images by Hybrid Encoding

  • Shuyu Yang1Email author,
  • Sunanda Mitra1,
  • Enrique Corona1,
  • Brian Nutter1 and
  • DJ Lee2
EURASIP Journal on Advances in Signal Processing20032003:619364

https://doi.org/10.1155/S1110865703211203

Received: 31 March 2002

Published: 14 April 2003

Abstract

Many common modalities of medical images acquire high-resolution and multispectral images, which are subsequently processed, visualized, and transmitted by subsampling. These subsampled images compromise resolution for processing ability, thus risking loss of significant diagnostic information. A hybrid multiresolution vector quantizer (HMVQ) has been developed exploiting the statistical characteristics of the features in a multiresolution wavelet-transformed domain. The global codebook generated by HMVQ, using a combination of multiresolution vector quantization and residual scalar encoding, retains edge information better and avoids significant blurring observed in reconstructed medical images by other well-known encoding schemes at low bit rates. Two specific image modalities, namely, X-ray radiographic and magnetic resonance imaging (MRI), have been considered as examples. The ability of HMVQ in reconstructing high-fidelity images at low bit rates makes it particularly desirable for medical image encoding and fast transmission of 3D medical images generated from multiview stereo pairs for visual communications.

Keywords

high fidelity hybrid encoding global codebook low bit rate multilevel wavelet feature statistics efficient retrieval of high-resolution medical images

Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, Texas Tech University
(2)
Department of Electrical and Computer Engineering, Brigham Young University

Copyright

© Copyright © 2003 Hindawi Publishing Corporation 2003