Medical Image Compression EECE 541 Multimedia

Systems

Harjot Pooni

Ashish Uthama

Victor Sanchez

Department of Electrical Engineering and Computer

Engineering

What are medical images ?Some examples MRI / FMRI (Function Magnetic Resonance)

Department of Electrical Engineering and Computer

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Medical Images Dynamic 3D Ultrasound

PET (Positron emission Tomography) CT (computerized Tomograhpy)

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Why compress medical images? Growing need for storage Efficient data transmission Telemedicine Tele-radiology applications Real time Tele-consultation. PACS (Picture archiving and communication

systems)

Department of Electrical Engineering and Computer

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Challenges unique to medical images. Compression Algorithms Lossy / Lossless Medical Images should always be stored in

lossless format. Erroneous Diagnostics and its legal

implications.

Department of Electrical Engineering and Computer

Engineering

Techniques usedCompression techniques may be classified into:

Lossy Lossless

Moreover, compression algorithms may be applied in the spatial domain or frequency domain

Compressed image e.g. WinZIP

Transform to frequency domain

Compressed image e.g. JPEG, JPEG2000

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JPEG 2000 and JPEG-LS High compression efficiency Lossless color transformations Progressive by resolution and quality Multiple component images ROI coding (static and dynamic) Error resilience capabilities Object oriented functionalities (coding, information,

embedding)

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Drawbacks of JPEG 2000 and JPEG-LS Only looks for redundancy in the frame. Does not exploit 3D and 4D redundancy

3D Redundancy

3D medical image

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4D Redundancy

Exploits temporal redundancy

. . . . . . . .

Time 1 Time 2 Time 3 Time n

4D medical image

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Ordering the data to exploit redundanciesTransform the problem domain: Convert 4D data to a sequence of 2D data

Volume

Time

Volume 1 Volume 2 Volume n

. . . . .

. . . . .

. . . . .

Slice 1

Slice 1

Slice 1

Slice s Slice s Slice s

Volume 1

Slice s

Volume 2 Volume n

. . . . .

Slice s Slice s

Slice 1 Slice 1 Slice 1

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3D-JPEG 2000 Part 10 – JP3D

“Part 10 is still at the Working Draft stage. It is concerned with the coding of three-dimensional data, the extension of JPEG 2000 from planar to volumetric images”

-http://www.jpeg.org/jpeg2000/j2kpart10.html

Some commercial vendors have already come out with 3D extensions of JPEG 2000

http://www.aware.com/products/compression/J2K3D.html

Provides guidelines for the use of JPEG 2000 for 3D data

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3D-JPEG 2000 The basic approach Wavelet transforms

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3D-JPEG 2000 The basic approach Reorder the 4D data by time or

volume

For each set, apply a 1D wavelet transform along the z axis

Apply JPEG 2000 on each transformed slice

H1

H2

L2

1D wavelet transform + JPEG 2000 coding = 3D-JPEG 2000

HL1

HH1LH1

HL2

HH2

LL2

LH2

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Drawbacks

Does not effectively use the redundancy in the 4th dimension (Temporal redundancy)

Movement of object between two slices would adversely effect performance

Object motion is significant in medical imaging

Patient movement Organ movement (Heart, Lung)

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Engineering

H.264/AVCLatest video coding standard uses motion compensation and estimation.

Source: www.vcodex.com

Department of Electrical Engineering and Computer

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Why use H.264? Better Intra frame compression

Medical images have comparatively more uniform areas Motion estimation and compensation

Address temporal redundancies Multiple frames may be used to predict a single frame.

Better performance Different block sizes for motion estimation (16x16, 16x8, 8x8)

Better performance! Improved entropy encoder

Better performance!!

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Approach One: H.264-VOL

Volume 1

Slice s

Volume 2 Volume n

. . . . .

Slice s Slice s

Slice 1 Slice 1 Slice 1

Apply H.264/AVC on slices arranged as shown above

Results:

Compression Technique Compression ratio

JPEG2000 2.55:1

JPEG-LS 3.06:1

3D-JPEG 2000(VOL) 3.15:1

H.264-VOL 3.89:1

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Approach Two: H.264-TIME

Volume 1 Volume 2 Volume n

. . . . .

. . . . .

. . . . .

Slice 1 Slice 1 Slice 1

Slice s Slice s Slice s

Apply H.264/AVC on slices arranged as shown above

Results:

Compression Technique Compression ratio

JPEG2000 2.55:1

JPEG-LS 3.06:1

3D-JPEG2000 (Time) 7.37:1

H.264-TIME 12.38:1

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H.264 applied across time H.264-TIMEVolume 1 Volume 2 Volume n

. . . . .

. . . . .

. . . . .

Slice 1 Slice 1 Slice 1

Slice s Slice s Slice s

Best compression performance

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How to improve compression efficiency?

Two ideas:

• Get the difference between consecutive image slices, then use H.264

• Calculate the residual frames, then use H.264

Main objective: reduce the energy content of eachimage slice.

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Difference between slices

DifferenceH.264

MC+

entropycoder

(CABAC)

. . . . .

. . . . .

. . . . .

. . .

Difference Difference

Slice 1

Slice s

Difference 2

Difference s

Difference 2

Difference s

Difference 2

Difference s

Volume 1 Volume 2 Volume n

Reference slice Reference slice

Slice 1

Slice s

Reference slice

Slice 1

Slice s

s coded bit-streams

Slice 1 Slice 2 Difference

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Residual frames

H.264 MC H.264 MC H.264 MC

H.264MC+

entropy coder

(CABAC)

. . . . .

. . . . .

. . . . .

MVs+ MVs+ MVs+

Volume 1 Volume 2 Volume n

Residual 2 Residual 2

Residual s Residual s

s coded bit-

streams

Slice 1

Slice s

Reference slice

Slice 1

Slice s

Reference slice

Slice 1

Slice s

Reference slice

Original slice Predicted Residual

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Results

CompressionTechnique

Improvement

H.264Difference

H.264Residual

3D-JPEG2000 100% 100%

H.264-TIME 20% 27%

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Future improvements

• Contextual encoding take into account characteristics of image

High motion

Low motion

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Low motion areas lossy

High motion areas lossless

Future improvements

Lossless

Lossy

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Encoding using “slices” (group of macroblocks): First slice for high motion areas Second slice for low motion areas

Slices may be encoded at different rates

Future improvements

First slice

Second slice

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Questions?