An experimental comparison of several lossless image coders for medical images

Abstract

Summary form only given. The output of medical imaging devices is increasingly digital and both storage space and transmission time of the images profit from compression. The introduction of PACS systems into the hospital environment fortifies this need. Since any loss of diagnostic information is to be avoided, lossless compression techniques are preferable. We present an experimental comparison of several lossless coders and investigate their compression efficiency and speed for different types of medical images. The coders are: five image coders (LJPEG, BTPC, FELICS, S+P, CALIC), and two general-purpose coders (GnuZIP, STAT). The medical imaging techniques are: CT, MRI, X-ray, angiography, mammography, PET and echography. Lossless JPEG (LJPEG), the current lossless compression standard, combines simple linear prediction with Huffman coding. Binary tree predictive coding (BTPC) is a multi-resolution technique which decomposes the image into a binary tree. The fast and efficient lossless image compression system (FELICS) conditions the pixel data on the values of the two nearest neighbours. Compression with reversible embedded wavelets (S+P) uses a lossless wavelet transform. The context-based, adaptive, lossless/nearly-lossless coding scheme for continuous-tone images (CALIC) combines non-linear prediction with advanced statistical error modelling techniques. GnuZIP uses LZ77, a form of sliding window compression. STAT is a PPM-lilte general-purpose compression technique. We give combined compression ratio vs. speed results for the different compression methods as an average over the different image types.