Improving 3D Medical Image Compression Efficiency Using Spatiotemporal Coherence

Advanced methodologies for transmitting compressed images, within acceptable ranges of transmission rate and loss of information, make it possible to transmit a medical image through a communication channel. Most prior works on 3D medical image compression consider volumetric images as a whole but fail to account for the spatial and temporal coherence of adjacent slices. In this paper, we set out to develop a 3D medical image compression method that extends the 3D wavelet difference reduction algorithm by computing the similarity of the pixels in adjacent slices and progressively compress only the similar slices. The proposed method achieves high-efficiency performance on publicly available datasets of MRI scans by achieving compression down to one bit per voxel with PSNR and SSIM up to 52:3 dB and 0:7578, respectively.

[1]  David S. Taubman,et al.  Transform and embedded coding techniques for maximum efficiency and random accessibility in 3-D scalable compression , 2005, IEEE Transactions on Image Processing.

[2]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[3]  Gandharba Swain,et al.  Volumetric medical image compression using 3D listless embedded block partitioning , 2016, SpringerPlus.

[4]  Jean-Christophe Lapayre,et al.  Medical Image Compression Approach Based on Image Resizing, Digital Watermarking and Lossless Compression , 2017, J. Signal Process. Syst..

[5]  Natarajan Sriraam,et al.  3-D medical image compression using 3-D wavelet coders , 2011, Digit. Signal Process..

[6]  Mohammed Gulam Ahamad,et al.  Performance analysis of three-dimensional medical image compression based on discrete wavelet transform , 2016, 2016 22nd International Conference on Virtual System & Multimedia (VSMM).

[7]  Nuno M. M. Rodrigues,et al.  Lossless Compression of Medical Images Using 3-D Predictors , 2017, IEEE Transactions on Medical Imaging.

[8]  Ayman AbuBaker,et al.  Comparison Study of Different Lossy Compression Techniques Applied on Digital Mammogram Images , 2016 .

[9]  William A. Pearlman,et al.  Three-Dimensional Wavelet-Based Compression of Hyperspectral Images , 2006, Hyperspectral Data Compression.

[10]  James E. Fowler.  QccPack: an open-source software library for quantization, compression, and coding , 2000, Proceedings DCC 2000. Data Compression Conference.

[11]  S. Manimurugan,et al.  A Lossless Compression Scheme for Grayscale Medical Images Using a P2-Bit Short Technique , 2017 .

[12]  F. Rawson.  Medical Imaging : Is the Growth Boom Over ? , 2022 .

[13]  Majid Rabbani,et al.  An overview of the JPEG 2000 still image compression standard , 2002, Signal Process. Image Commun..

[14]  Abir Jaafar Hussain,et al.  Image compression techniques: A survey in lossless and lossy algorithms , 2018, Neurocomputing.

[15]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[16]  P. Alam.  ‘G’ , 2021, Composites Engineering: An A–Z Guide.

[17]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[18]  Jun Tian,et al.  A lossy image codec based on index coding , 1996, Proceedings of Data Compression Conference - DCC '96.

[19]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[20]  Nam Ling,et al.  Lossy and lossless intra coding performance evaluation: HEVC, H.264/AVC, JPEG 2000 and JPEG LS , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[21]  P. Alam.  ‘L’ , 2021, Composites Engineering: An A–Z Guide.

[22]  J. Cornelis,et al.  Wavelet coding of volumetric medical datasets , 2003, Proceedings. International Conference on Image Processing.

[23]  Peter Schelkens,et al.  Wavelet based volumetric medical image compression , 2015, Signal Process. Image Commun..

[24]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.