Efficient medical image compression technique for telemedicine considering online and offline application

Abstract

Telemedicine characterized by transmission of medical data and images between users is one of the emerging fields in medicine. Huge bandwidth is necessary for transmitting medical images over the internet. Resolution factor and number of images per diagnosis makes even the size of the images that belongs to a single patient to be very large in size. So there is an immense need for efficient compression techniques for use in compressing these medical images to decrease the storage space and efficiency of transfer the images over network for access to electronic patient records. This project summarizes the different transformation methods used in compression as compression is one of the techniques that reduces the amount of data needed for storage or transmission of information. This paper outlines the comparison of transformation methods such as DPCM (Differential Pulse Code Modulation), and prediction improved DPCM transformation step of compression and introduced a transformation which is efficient in both entropy reduction and computational complexity. A new method is then achieved by improving the perdiction model which is used in lossless JPEG. The prediction improved transformation increases the energy compaction of prediction model and as a result reduces entropy value of transformed image. After transforming the image Huffman encoding used to compress the image. As a result, the new algorithm shows a better efficiency for lossless compression of medical images, especially for online applications. The result is analyzed using MATLAB and implemented in hardware.