A memory-assisted lossless compression algorithm for medical images.

Rapid growth of emerging medical applications such as e-health and tele-medicine requires fast, low cost, and often lossless access to massive amount of medical images and data over bandlimited channels. In this paper, we first show that significant amount of correlation and redundancy exist across different medical images. Such a correlation can be utilized to achieve better compression, and consequently less storage and less communication overhead on the network. We propose a novel memory-assisted compression technique, as a learning-based universal coding, which can be used to complement any existing algorithm to further eliminate redundancies across images. The approach is motivated by the fact that, often in medical applications, massive amount of correlated images from the same family are available as training data for learning the dependencies and deriving appropriate reference models. Such models can then be used for compression of any new image from the same family. In particular, Principal Component Analysis (PCA) is applied on a set of images from training data to form the required reference models. The proposed memory-assisted compression allows each image to be processed independently of other images, and hence allows individual image access and transmission. Experimental results on Xray images show that the proposed algorithm achieves 20% improvement over and above traditional lossless image compression methods reported in the literature.