PERFORMANCE ANALYSIS OF REVERSIBLE IMAGE COMPRESSION TECHNIQUES FOR HIGH-RESOLUTION DIGITAL TELERADIOLOGY

In recent years, there have been a number of studies addressing both reversible and irreversible compression of medical images ranging from 256 x 256 to 2048 x 2048 pixels in spatial resolution. There is a need to address the high-resolution end of the image categories, namely mammograms and chest X-rays, which require resolution of the order of 4096 x 4096 pixels. Further, data compression schemes for most medical applications have to be information-preserving or reversible. In this paper, the performance of a number of block-based, reversible, compression algorithms suitable for compression of very large-format images (4096 x 4096 pixels or more) is compared to that of a novel two-dimensional linear predictive coder developed by extending the multichannel version of the Burg algorithm to two dimensions. The compression schemes implemented are: Huffman coding, Lempel-Ziv coding, arithmetic coding, two-dimensional linear predictive coding (in addition to the aforementioned one), transform coding using discrete Fourier-, discrete cosine-, and discrete Walsh transforms, linear interpolative coding, and combinations thereof. We discuss the performance of these coding techniques with a few mammograms and chest radiographs digitized to sizes up to 4096 x 4096, 10 b pixels. We have achieved compression from 10 bits to 2.5-3.0 b/pixel on these images without any loss of information. The modified multichannel linear predictor out-performs the other methods while offering certain advantages in implementation.