Abstract
Retrieval of relevant information and its efficient transmission over the Internet to worldwide users are of utmost interest in many applications such as telemedicine, video conferencing, distance education, to name a few. Content-based source encoding is, however, essential in enhancing information retrieval. Despite some significant work done in this area, indexing and retrieval of medical image data still pose a challenging problem since distinct features are not always present in such data sets. We present a novel hybrid multi-scale vector quantizer (HMVQ) whose codebook is generated by neuro-fuzzy clustering of salient information features in the wavelet domain. Our codec incorporates multi-scale feature extraction, vector quantization codebook training and detail-preserving residual scalar quantization. The performance of this new vector encoder, namely, HMVQ, surpasses that of the well-known scalar coder, the Set Partitioning in Hierarchical Trees (SPIHT) in the fidelity of reconstructed data at all bit rates. Our results also demonstrate that the performance of such encoder is equivalent to an optimized statistical approach while, providing a drastic reduction in execution time. Efficiency in computational cost is of great significance while considering future advances in visual communications using multiview 3-D auto-stereoscopic systems.
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References
Antonini M, Barlaud M, Mathieu P, Daubechies I (1992) Image coding using wavelet transform. IEEE Trans. on Image Processing 1(2):205–220
Antonini M, Gaidon T, Mathieu P, Barlaud M (1994) Wavelet transform and image coding. In: Baulaud M (ed) Wavelet in image communication, Elsevier, Amsterdam
Berger T (1971) Rate Distortion Theory. Englewood Cliffs, Prentice-Hall, NJ
Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms, Plenum Press NY
Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. J Computer vision, graphics and image processing 37:54–115
Carpenter GA, Grossberg S (1987) Art-2: self organization of stable category recognition codes for analog input patterns. J Appl. Opt. 26: 4919–4930
Carpenter GA, Grossberg S (1990) Art-3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. J Neural networks, 3:129–152
Castellanos R, Castillo H, Mitra S (1999) Performance of nonlinear methods in medical image restoration. SPIE proceedings on nonlinear image processing 3646
Cosman PC, Perlmutter SM, Perlmutter KO (1995) Tree-structured vector quantization with significance map for wavelet image coding. In: Proceeding of data compression conference, Snowbird Utah
Daubechies I (1992), Ten lectures on wavelets in CBMS conference on wavelets, Society for Industrial and Applied Mathematics 61
Gersho A, Gray RM (1992) Vector quantization and signal compression. Kluwer Boston MA
Gray RM, Neuhoff DL (1998) Quantization. IEEE Transactions on information theory 44(6): 2325–2383
Johnson KA, Becker JA, (2001, July). The whole brain atlas, normal brain, Atlas of normal structure and blood flow. [Online] Available: http://www.med.harvard.edu/ AANLIB/cases/caseM/mrl_t/
Linde YL, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans. Commun. 28: 84–95
Lyons DF, Neuhoff DL, Hui D (1993) Reduced storage tree-structured vector quantization. In: Proc. IEEE Conf. Acoustics, Speech, Signal Proc. 5:602–605 Minneapolis
Mitra S, Yang S (1998) High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images. J Electronic Imaging 11(4) Suppl. 2:24–30
Montréal Neurological Institute, McGill University (2001) BrainWeb: simulated brain database. Montréal Neurological Institute, McGill University, (2001, May). BrainWeb: Simulated Brain Database. [Online] Available: http://www.bic.mni.cgill.ca/brainweb
Mukherjee D, Mitra SK (1998) Vector set partitioning with classified successive refinement VQ for embedded wavelet image coding. In: Proc. IEEE international symposium on circuits & systems: 25–28, Monterey CA
Nasrabadi N, King R (1988) Image coding using vector quantization: a review. IEEE Trans. commun. 36(8): 957–971
National Library of Medicine (2002) World wide web medical information retrieval system. [Online] Available: http://archive.nlm.nih.gov/proj/webmirs/
Newton SC, Pemmaraju S, Mitra S (1992) Adaptive fuzzy leader clustering of complex data sets in pattern recognition. IEEE Trans. Neural Networks 3:794–800
Rose K (1998) Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. In: Proc. of IEEE 86(11)
Said A, Pearlman WA (1996) A new,fast and effcient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits and systems for video technology 6(3):243–250
Shapiro JM (1993) Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. signal processing, 41(12): 3445–3462
Skodras A, Christopoulos C, Ebrahimi T (2001) The JPEG2000 still image compression standard. IEEE signal processing magazine Sept: 36–58
Strang G, Nguyen T (1996) Wavelets and filter banks. Wellesley-Cambridge Press, Wellesley MA
Van Dyck RE, Rajala SA (1994) Subband/VQ coding of color images with perceptually optimal bit allocation. IEEE Trans. circuits and systems for viedo techn. 4(1): 68–82
Vetterli M, Kovacevic J (1995) Wavelets and subband coding, Prentice Hall, Englewood Cliffs NJ
Yang S, Mitra S (2001) Rate distortion in image coding from embedded optimization constraints in vector quantization. The International Joint INNS-IEEE Conference on Neural Networks, Washington DC
Lotfi A. Zadeh, (1973) “Outline of a new approach to the analysis of complex systems and decision processes” IEEE Trans. On Systems, Man, and Cybernetics, SMC-3 (1): 28–44
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Yang, S., Mitra, S. (2004). Content Based Vector Coder for Efficient Information Retrieval. In: Nikravesh, M., Azvine, B., Yager, R., Zadeh, L.A. (eds) Enhancing the Power of the Internet. Studies in Fuzziness and Soft Computing, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45218-8_13
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DOI: https://doi.org/10.1007/978-3-540-45218-8_13
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