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Near Lossless Compression for 3D Radiological Images Using Optimal Multilinear Singular Value Decomposition
S Boopathiraja1, P Kalavathi1, S Deoghare2,3
1Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to Be University), Gandhigram, 624 302, Tamil Nadu, India.
Journal of Digital Imaging
|August 29, 2022
Summary
A novel 3D image compression method, 3D-VOI-OMLSVD, offers near lossless compression for teleradiology. This technique significantly improves compression rates and reduces bit rates compared to JPEG and JPEG2000.
Area of Science:
- Medical Imaging
- Computer Science
- Signal Processing
Background:
- Effective teleradiology relies on efficient storage and transmission of high-compression 3D radiological images.
- Current methods face challenges in maintaining image quality during compression and decompression.
Purpose of the Study:
- To propose a near lossless 3D image volume compression method for teleradiology.
- To enhance the efficiency of 3D radiological image storage and transmission.
Main Methods:
- Developed 3D-VOI-OMLSVD, a method utilizing optimal multilinear singular value decomposition.
- Implemented selective bounding volume (SBV) for Volume of Interest (VOI) extraction.
- Employed adaptive binary range coder (ABRC) for entropy encoding of decomposed VOI.
Main Results:
- Achieved high compression rates (CR) up to 37.31 and low bit rates (BR) as low as 0.21.
- Maintained high structural similarity index (SSIM) scores averaging 0.9868.
- Demonstrated superior performance over JPEG and JPEG2000 in CR and BR, with decoding times under 1 second.
Conclusions:
- 3D-VOI-OMLSVD provides a robust solution for high-quality 3D radiological image compression.
- VOI extraction significantly boosts compression efficiency and reduces processing time.
- The method is well-suited for teleradiology applications requiring efficient data handling.
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