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

Authors:
Shuyu Yang;Sunanda Mitra;Enrique Corona;Brian Nutter;D. J. Lee
Affiliations:
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX;Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX;Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX;Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX;Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2003

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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.