Lossless medical image compression using integer transforms and predictive coding technique

Abstract

The future of healthcare delivery systems and telemedical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing and communication techniques. E-health was born with the integration of networks and telecommunications when dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis. Healthcare systems in recent years rely on images acquired in two dimensional (2D) domain in the case of still images, or three dimensional (3D) domain for volumetric images or video sequences. Images are acquired with many modalities including X-ray, positron emission tomography (PET), magnetic resonance imaging (MRI), computed axial tomography (CAT) and ultrasound. Medical information is either in multidimensional or multi resolution form, this creates enormous amount of data. Efficient storage, retrieval, management and transmission of this voluminous data is extremely complex. One of the solutions to reduce this complex problem is to compress the medical data losslessly so that the diagnostics capabilities are not compromised. This report proposes techniques that combine integer transforms and predictive coding to enhance the performance of lossless compression. The performance of the proposed techniques is evaluated using compression measures such as entropy and scaled entropy.