Lossless compression of medical images

Abstract

Lossless compression of magnetic resonance images is reviewed using both the theoretical and implementation models. The compression level of selected algorithms (Lempel-Ziv and Huffman) are compared against the first-order, second-order, and conditional entropies. It is found that the compression upper limit for Huffman is the first-order entropy and for Lempel-Ziv, the second-order or first-order conditional entropies. The experiments showed that the second-order and conditional entropies were lower per pixel than the first-order, suggesting a certain amount of dependencies between the adjacent pixels. As a result, the Lempel-Ziv achieved more compression than the Huffman. The first transformation (difference coding) improves the compression level by 6% for Huffman and 1% for Lempel-Ziv. In a second transformation, where images are split by their upper and lower bytes of each pixel, Lempel-Ziv performs better on the higher byte and Huffman performs better on the lower byte.< >