Lossless compression of medical images using Burrows-Wheeler Transformation with Inversion Coder

Area of Science:

• Medical Imaging
• Data Compression
• Computer Science

Background:

• Medical imaging generates large datasets requiring efficient storage and transmission.
• Lossless compression is essential for medical images to prevent information loss.
• Existing compression methods may not be optimal for the unique characteristics of medical imagery.

Purpose of the Study:

• To evaluate the effectiveness of the Burrows-Wheeler Transformation with an Inversion Coder (BWIC) for lossless medical image compression.
• To compare BWIC's performance against established medical image compression algorithms.

Main Methods:

• Implementation and testing of the Burrows-Wheeler Transformation with an Inversion Coder (BWIC).
• Comparative analysis of BWIC against JPEG-LS and JPEG-2000 using medical image datasets.
• Assessment of compression ratios and processing time.

Main Results:

• The Burrows-Wheeler Transformation with an Inversion Coder (BWIC) operates in linear time.
• BWIC demonstrates superior lossless compression rates compared to JPEG-LS and JPEG-2000.
• The algorithm effectively reduces storage and transmission requirements for high-resolution medical images.

Conclusions:

• The Burrows-Wheeler Transformation with an Inversion Coder (BWIC) is a highly efficient lossless compression algorithm for medical imaging.
• BWIC offers a promising alternative for managing large medical image data, improving storage and transmission efficiency.