Lossless Medical Image Compression by Integer Wavelet and Predictive Coding

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. When dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis we required to considered many parameters for telemedical application. E-health was born with the integration of networks and telecommunications. In recent years, healthcare systems rely on images acquired in two-dimensional domains in the case of still images or three-dimensional domains for volumetric video sequences and images. Images are acquired by many modalities including X-ray, magnetic resonance imaging, ultrasound, positron emission tomography, and computed axial tomography (Sapkal and Bairagi, 2011). Medical information is either in multidimensional or multiresolution form, which creates enormous amount of data. Retrieval, efficient storage, management, and transmission of these voluminous data are highly complex. One of the solutions to reduce this complex problem is to compress the medical data without any loss (i.e., lossless). Since the diagnostics capabilities are not compromised, this technique combines integer transforms and predictive coding to enhance the performance of lossless compression. The proposed techniques can be evaluated for performance using compression quality measures. 1. Introduction Applications involve image transmission within and among health care organizations using public networks. In addition to compressing the data, this requires handling of security issues when dealing with sensitive medical information system. Compressing medical data includes high compression ratio and the ability to decode the compressed data at various resolutions. In order to provide a reliable and efficient means for storing and managing medical data computer-based archiving systems such as Picture Archiving and Communication Systems (PACSs) and Digital Imaging and Communications in Medicine (DICOM), standards were developed with the explosion in the number of images acquired for diagnostic purposes; the importance of compression has become invaluable in developing standards for maintaining and protecting medical images and health records. Compression offers a means to reduce the cost of storage and to increase the speed of transmission. Thus, medical images have attained a lot of attention towards compression. These images are very large in size and require a