Approaches for enhancing the compression performance of Medical images employing image representation and prediction Techniques

Abstract

In current days, medical images upswing due to several main disease prognosis. Medical images require a huge amount of memory and took extra bandwidth for storage as well as transmission. Due to the vast progress in multimedia data storage and communication technologies, the medical image compressing still remains demanding. Digital imaging modalities such as CT and MRI imaging are used to capture images from within our body in digital forms. newlineThe main principle of image compression is to decrease the excess of image data and to store or transmit it in a suitable form. Compression of an image significantly differs with respect to the compression of a raw binary data. Therefore, in order to abate the exorbitant measures of data formed from these imaging, compression is considered crucial for storage and communication purposes. Also, extensive datasets need huge space and great network scopes in transmission especially in the applications of telemedicine and teleradiology. Hence, compression is significant in medical imaging. In addition to compression, quality of the images also plays an imperative role in prosperous analysis. Moreover, the time required to compress the images before transmitting should be less. newlineIn telemedicine, the need for data storage and bandwidth requirements escalates, that is, lossless compression techniques turns into fundamental. The principal objective of this research is to provide compact representation and removing redundancy that assists in attempting high compression performance for encoding medical images newline newline