3D medical image compression based on multiplierless low-complexity RKLT and shape-adaptive wavelet transform

  • Authors:

  • Lei Wang;Jiaji Wu;Licheng Jiao;Guangming Shi

  • Affiliations:

  • Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China;Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi'an, P.R. China

  • Venue:
  • ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
  • Year:
  • 2009

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Abstract

A multiplierless low complexity reversible integer Karhunen-Loève transform (Low-RKLT) is proposed based on matrix factorization. Conventional methods based on KLT suffer from high computational complexity and unability of applying in lossless medical image compression. To solve the two problems, multiplierless Low-RKLT is investigated using multi-lifting in this paper. Combined with ROI coding method, we have proposed a progressive lossyto-lossless ROI compression method for three dimensional (3D) medical images with high performance. In our proposed method Low-RKLT is used for the inter-frame decorrelation after SA-DWT in the spatial domain. Simulation results show that, the proposed method performs much better in both lossless and lossy compression than 3DDWT- based method.