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Computational 2D and 3D Medical Image Data Compression Models

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Abstract

In this world of big data, the development and exploitation of medical technology is vastly increasing and especially in big biomedical imaging modalities available across medicine. At the same instant, acquisition, processing, storing and transmission of such huge medical data requires efficient and robust data compression models. Over the last 2 decades, numerous compression mechanisms, techniques and algorithms were proposed by many researchers. This work provides a detailed status of these existing computational compression methods for medical imaging data. Appropriate classification, performance metrics, practical issues and challenges in enhancing the two dimensional (2D) and three dimensional (3D) medical image compression arena are reviewed in detail.

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Abbreviations

2D:

Two dimensional

3D:

Three dimensional

AI:

Artificial intelligence

BPNN:

Back propagation neural network

BWT:

Burrows wheeler transform

CAGR:

Compound annual growth rate

CANDECOMP:

Canonical decomposition

CC:

Correlation coefficient

CCR:

Computational complexity reduction

CLEBP:

Color listless embedded block partitioning

CPTBC:

CANDECOMP/PARAFAC tensor based compression

CR:

Compression ratio

CT:

Computed tomography

DICOM:

Digital imaging and communications in medicine

DIP:

Digital image processing

DLWIC:

Distortion limited wavelet image codec

EHR:

Electronic health record

ERLE:

Enhanced run length encoding

FSTD:

Fiber sampling tensor decomposition

HOOI:

Higher order orthogonal iteration

HVS:

Human visual system

iDTT:

Integer discrete Tchebichef transform

JBIG:

Joint bi-level image expert group

MHE:

Merging based Huffman encoding

MIW:

Medical image watermarking

MLSVD:

Multilinear singular value decomposition

MRI:

Magnetic resonance imaging

Nifti:

Neuroimaging informatics technology initiative

ONC:

Office of National Coordinator for health information technology

PACS:

Picture archiving and communication system

PET:

Positron emission tomography

RG:

Region growing

RMSE:

Root mean square error

SA-DWT:

Shape-adaptive discrete wavelet transforms

SA-ROI:

Scaling based ROI

SNR:

Signal to noise ratio

SPIHT:

Set partitioning in hierarchical trees

SVD:

Singular value decomposition

TC-VQ:

Transform coding and vector quantization

UIQI:

Universal Image Quality Index

US:

Ultra sound

3D-ESCOT:

Three dimensional cube splitting embedded block coding with optimized truncation

3DHLCK:

3D hierarchical listless embedded block

ALZ:

Adaptive Lempel–Ziv

BPG:

Better portable graphics

BPP:

Bit per pixel

BTF:

Bidirectional texture functions

CALIC:

Context based arithmetic lossless imaging codec

CE:

Capsule endoscopy

CUR:

Column row decomposition

DCT:

Discrete cosine transform

DMA:

Distortion minimization algorithm

DST:

Discrete sine transform

DWT:

Discrete wavelet transform

EBCOT:

Embedded block coding with optimized truncation

EEG:

Electroencephalograms

EZW:

Embedded zerotree wavelet

HINT:

Hierarchical interpolation

HOSVD:

Higher order singular value decomposition

JPEG2000:

Joint Photographic Experts Group 2000

KLT:

Karhunen–Loeve transform

LIP:

List of insignificant pixels

LISt:

List of insignificant sets

LNS:

Logarithmic number system

LSP:

List of significant pixels

MC-EEG:

Multichannel electroencephalogram

MFT:

Move to front transform

MNF:

Maximum noise fraction

MPEG:

Motion Picture Experts Group

MRG:

Modified region growing

MRP:

Minimum rate predictors

MSE:

Mean square error

PCA:

Principal component analysis

PLTD:

Patch-based low-rank tensor decomposition

SBV:

Selective bounding volume

SC:

Structural content

SLIC:

Segmentation based lossless image coding

SPECK:

Set partitioned embedded block coder

SPHIT:

Set partitioning in hierarchical trees

SSIM:

Structural Similarity Index

VQ:

Vector quantaization

WDD:

Weighted diagnostic distortion

3D BISK:

Three dimensional binary set splitting with k-D trees

3D-DHT:

3D discrete Hartley transform

AAC:

Adaptive arithmetic coding

BPV:

Bit per voxel

BWCA:

Burrow Wheeler Compression Algorithm

CABAC:

Context adaptive binary arithmetic coding

CDF:

Cohen–Daubechies–Feauveau

CRLE:

Conventional run length encoding

DTT:

Discrete Tchebichef transform

DWT-BP:

DWT with BACK PROPAGATION

ECG:

Electrocardiogram

FSTD:

Fiber sampling tensor decomposition

GOP:

Group of pictures

HEVC:

High efficiency video coding

HSI:

Hyper spectral images

HWT:

Haar wavelet transform

IWT:

Integer wavelet transform

JBIG:

Joint Bi-level Image Expert Group

LEBP:

Listless embedded block partitioning

LT-TD:

Lapped transform Tucker decomposition

MSSIM:

Mean Structural Similarity Index

MSSIM:

Multi-Scale Structural Similarity Index

ORLE:

Optimized run length encoding

PFCM:

Possiblistic Fuzzy C-Means Clustering method

PIT:

Progressive image transmission

PNG:

Portable network graphics

PRD:

Percent rate distortion

PRD:

Percentage root-mean squared difference

PSNR:

Peak signal to noise ratio

RLC:

Run LENGTH CODER

ROI:

Region of interest

SA-ROI:

Shape adaptive region of interest

SWT:

Stationary wavelet transforms

TCS:

Tensor compressive sensing

TSUID:

Transfer Syntax Unique Identification

VOI:

Volume of interest

WDD:

Weighted Diagnostic Distortion Index

ZTE:

Zero tree entropy

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Funding

PK supported by Council of Scientific & Industrial Research (CSIR), File Number: 25(0304)/19/EMR-II/, Human Resource Development Group, and Government of India. VBSP is supported by NCATS/NIH Grant U2CTR002818, NHLBI/NIH grant U24HL148865, NIAID/NIH grant U01AI150748, Cincinnati Children's Hospital Medical Center-Advanced Research Council (ARC) Grants 2018–2020, and the Cincinnati Children's Research Foundation–Center for Pediatric Genomics (CPG) Grants 2019–2021.

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Authors and Affiliations

  1. Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, 624 302, India

    S. Boopathiraja, V. Punitha & P. Kalavathi

  2. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA

    V. B. Surya Prasath

  3. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45257, USA

    V. B. Surya Prasath

  4. Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA

    V. B. Surya Prasath

  5. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45221, USA

    V. B. Surya Prasath

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  1. S. Boopathiraja
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  4. V. B. Surya Prasath
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Boopathiraja, S., Punitha, V., Kalavathi, P. et al. Computational 2D and 3D Medical Image Data Compression Models. Arch Computat Methods Eng 29, 975–1007 (2022). https://doi.org/10.1007/s11831-021-09602-w

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