Related Experiment Video
Updated: Aug 4, 2025
11:00
Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
Published on: July 19, 2016
11.7K
An Empirical Selection of Wavelet for Near-lossless Medical Image Compression
Punitha Viswanathan1, Kalavathi Palanisamy1
1Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tami Nadu, India.
Current Medical Imaging
|March 31, 2023
Summary
This study optimizes medical image compression using Discrete Wavelet Transform (DWT) sub-banding. Optimal wavelet selection with the SPIHT scheme enhances compression performance while preserving crucial image details for telemedicine applications.
Area of Science:
- Signal Processing
- Image Compression
- Medical Imaging
Background:
- Telemedicine increasingly relies on efficient medical image handling.
- High-quality medical image compression is crucial for data transmission and storage.
- Near-lossless compression offers a balance between compression ratio and image fidelity.
Purpose of the Study:
- To analyze Discrete Wavelet Transform (DWT) sub-banding for medical image compression.
- To identify optimal wavelets for subband thresholding and compression performance.
- To evaluate wavelet-based near-lossless compression for medical images.
Main Methods:
- Sub-banding analysis of DWT using various wavelet types.
- Optimal wavelet selection for subband thresholding.
- Application of Set Partitioning In Hierarchical Trees (SPIHT) compression scheme.
- Performance evaluation using Peak Signal to Noise Ratio (PSNR), Bits Per Pixel (BPP), Compression Ratio, and zero counts.
Main Results:
- Different wavelets exhibit varying compression efficiencies for medical images.
- Optimal wavelet selection significantly improves compression performance.
- The SPIHT scheme effectively assesses wavelet-based compression metrics.
- Selected wavelets retain essential image information for near-lossless compression.
Conclusions:
- Wavelet-based sub-banding and thresholding are effective for medical image compression.
- Optimal wavelet selection is key to achieving high compression ratios with preserved image quality.
- The proposed approach supports efficient near-lossless compression vital for telemedicine.
