Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems.

[1]  M. Moorthi,et al.  An Improved Algorithm for Medical Image Compression , 2011 .

[2]  N. Zulpe,et al.  GLCM Textural Features for Brain Tumor Classification , 2012 .

[3]  G. Bjontegaard,et al.  Calculation of Average PSNR Differences between RD-curves , 2001 .

[4]  F. Bossen,et al.  Common test conditions and software reference configurations , 2010 .

[5]  Marios S. Pattichis,et al.  M-health medical video communication systems: An overview of design approaches and recent advances , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[6]  K.V. Sridhar Implementation of prioritised ROI coding for medical image archiving using JPEG2000 , 2008, 2008 International Conference on Signals and Electronic Systems.

[7]  Marios S. Pattichis,et al.  An Effective Ultrasound Video Communication System Using Despeckle Filtering and HEVC , 2015, IEEE Journal of Biomedical and Health Informatics.

[8]  L. Nithyanandan,et al.  Medical video communication using modified HEVC over WiMAX network , 2014, 2014 International Conference on Communication and Signal Processing.

[9]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[10]  Hayit Greenspan,et al.  Texture feature based liver lesion classification , 2014, Medical Imaging.

[11]  Marios S. Pattichis,et al.  High efficiency video coding for ultrasound video communication in m-health systems , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[13]  P. Venkata Krishna,et al.  Global Trends in Information Systems and Software Applications , 2012, Communications in Computer and Information Science.

[14]  Chen Hui Research on methods and application of texture analysis of medical images , 2013 .

[15]  Marios S. Pattichis,et al.  HEVC encoding for reproducible medical ultrasound video diagnosis , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[16]  Touradj Ebrahimi,et al.  [JCT-VC contribution] AhG4: Subjective evaluation of HEVC intra coding for still image compression , 2013 .

[17]  Gary J. Sullivan,et al.  Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.