Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction

J Digit Imaging. 2016 Dec;29(6):706-715. doi: 10.1007/s10278-016-9892-y.

Abstract

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

Keywords: Adaptive block size; Block searching; Lossless Hadamard transform; Near-lossless compression; Spatial prediction.

MeSH terms

  • Algorithms*
  • Data Compression*
  • Humans
  • Magnetic Resonance Imaging / statistics & numerical data
  • Signal-To-Noise Ratio
  • Tomography, X-Ray Computed / statistics & numerical data