Lossless compression algorithm of medical image based on weighted template matching prediction

In order to enhance the transmission and storage efficiency of medical images, this paper proposes a lossless compression algorithm designed for medical imaging. The algorithm processes input images using an improved weighted template matching predictor, yielding error calculation results and symbol matrices. These outcomes are then entropy-encoded using PNG and Deflate encoding for the predictive results, achieving compression. Experimental verification was conducted using a test dataset comprised of cross-sectional images extracted from multiple scan sequences. The results indicate that the proposed algorithm achieves an average compression ratio of 1:3.00926, outperforming other lossless compression algorithms based on linear prediction, such as PNG and TIFF.