Multilevel wavelet feature statistics for efficient retrieval, transmission, and display of medical images by hybrid encoding

Shuyu Yang, Sunanda Mitra, Enrique Corona, Brian Nutter, D. J. Lee

Research output: Contribution to journalArticle

9 Scopus citations

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.

Original languageEnglish
Pages (from-to)449-460
Number of pages12
JournalEurasip Journal on Applied Signal Processing
Volume2003
Issue number5
DOIs
StatePublished - Apr 1 2003

Keywords

  • Efficient retrieval of high-resolution medical images
  • Global codebook
  • High fidelity hybrid encoding
  • Low bit rate
  • Multilevel wavelet feature statistics

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