Skip to content

Advertisement

  • Research Article
  • Open Access

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

  • 1Email author,
  • 1,
  • 1,
  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20032003:619364

https://doi.org/10.1155/S1110865703211203

  • Received: 31 March 2002
  • Published:

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, Lubbock, TX 79409-3102, USA
(2)
Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, USA

Copyright

Advertisement