Digital techniques are used more often than ever in a variety of fields. Medical information management is one of the largest digital technology applications. It is desirable to have both a large data storage resource and extremely fast data transmission channels for communication. On the other hand, it is also essential to compress these data into an efficient form for storage and transmission. A variety of data compression techniques have been developed to tackle a diversity of situations. A digital value decomposition method using a splitting and remapping method has recently been proposed for image data compression. This method attempts to employ an error-free compression for one part of the digital value containing highly significant value and uses another method for the second part of the digital value. We have reported that the effect of this method is substantial for the vector quantization and other spatial encoding techniques. In conjunction with DCT type coding, however, the splitting method only showed a limited improvement when compared to the nonsplitting method. With the latter approach, we used a nonoptimized method for the images possessing only the top three-most-significant- bit value (3MSBV) and produced a compression ratio of approximately 10:1. Since the 3MSB images are highly correlated and the same values tend to aggregate together, the use of area or contour coding was investigated. In our experiment, we obtained an average error-free compression ratio of 30:1 and 12:1 for 3MSB and 4MSB images, respectively, with the alternate value contour coding. With this technique, we clearly verified that the splitting method is superior to the nonsplitting method for finely digitized radiographs.