Lossless compression based on hierarchical extrapolation for biomedical imaging applications

TLDR: A lossless image compression based on hierarchical extrapolation for medical images using Haar transform and through Embedded Encoding technique proves to be lossless and as well perform better for a variety of images including CT scan, MRI and ultrasound biomedical images than the existing schemes.

Abstract: The imaging systems like CT, MRI scan exhibits huge amount of digital data and therefore compression becomes crucial for storage and communication resolves. Most of the recent compression schemes offers a very high compression ratio with significant loss of image quality and do not always perform better for all sets of similar images. This work aims at resolving this issue, which serves as the motivation. This paper presents a lossless image compression based on hierarchical extrapolation for medical images using Haar transform and through Embedded Encoding technique. The compression technique proves to be lossless and as well perform better for a variety of images including CT scan, MRI and ultrasound biomedical images than the existing schemes. The performance metrics namely Peak Signal to Noise Ratio, Compression ratio and mean square error values are computed for the compressed image for evaluation. The performance metrics attained through the proposed algorithm is bench marked with JPEG 2000. The result section of this paper brings forth the relative improvement offered by the proposed logic.