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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 7567
School of Cognitive Sciences
  Title:   Adaptive vector quantization of MR images using online k-means algorithm
  Author(s): 
1.  A. Shademan
2.  M.A. Zia
  Status:   In Proceedings
  Proceeding: Int. Symp. On Optical Sciences and Technology: Applications of Digital Image Processing XXIV
  Year:  2001
  Supported by:  IPM
  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

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