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Proceedings Paper

Adaptive vector quantization of MR images using online k-means algorithm
Author(s): Azad Shademan; Mohammad Amin Zia
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Paper Abstract

The k-means algorithm is widely used to design image codecs using vector quantization (VQ). In this paper, we focus on an adaptive approach to implement a VQ technique using the online version of k-means algorithm, in which the size of the codebook is adapted continuously to the statistical behavior of the image. Based on the statistical analysis of the feature space, a set of thresholds are designed such that those codewords corresponding to the low-density clusters would be removed from the codebook and hence, resulting in a higher bit-rate efficiency. Applications of this approach would be in telemedicine, where sequences of highly correlated medical images, e.g. consecutive brain slices, are transmitted over a low bit-rate channel. We have applied this algorithm on magnetic resonance (MR) images and the simulation results on a sample sequence are given. The proposed method has been compared to the standard k-means algorithm in terms of PSNR, MSE, and elapsed time to complete the algorithm.

Paper Details

Date Published: 7 December 2001
PDF: 8 pages
Proc. SPIE 4472, Applications of Digital Image Processing XXIV, (7 December 2001); doi: 10.1117/12.449776
Show Author Affiliations
Azad Shademan, Institute for Studies in Theoretical Physics and Mathematics and Univ. of Tehran (Iran)
Mohammad Amin Zia, Institute for Studies in Theoretical Physics and Mathematics (Iran)


Published in SPIE Proceedings Vol. 4472:
Applications of Digital Image Processing XXIV
Andrew G. Tescher, Editor(s)

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