George R. Thoma

Image Compression Approaches for the Visible Human Dataset

George R. Thoma Lister Hill National Center for Biomedical Communication National Library of Medicine

It is anticipated that the Visible Human image dataset will find many applications, both as a research tool as well as a key component for products such as instructional packages and anatomic atlases. Its large size, however, precludes rapid transfer over the Internet as well as convenient distribution by conventional CDROM or other optical media.

The major contributor to the size of the dataset by far is the color cryosection slices. The 2048 x 1216 x 24 bits CCD scanned images for the male amount to 15 GB, and about 40 GB for the female. While this 55 GB dataset is in itself hardly inconsequential, the problem is greatly exacerbated once the 70mm photographs of the slices are scanned at 4096 x 2700 pixels, at which point the corresponding set will amount to 60 GB and 170 GB for the male and female respectively, for a total of 230 GB.

A key consideration in compressing the dataset is the retention of image quality in the reconstructed images for a wide range of anticipated applications. Conventional lossless compression, which achieves this goal, has the disadvantage of very modest compression ratio (CR), no more than 2 or 3 [Pennebaker]. This will not make much of a dent in the ease of distribution of 230 GB of Visible Human image data. A variety of lossy compression techniques are available, some of them standardized: e.g., JPEG and MPEG. JPEG (Joint Photographic Experts Group), widely used for commercial imagery, has the advantage of being available as commercial products, but also has the disadvantage of creating blocky (or, blocking) artifacts at respectable compression ratios, i.e., over 10, a consequence of it 8 x 8 discrete cosine transform (DCT) coding scheme.

Most current research efforts in lossy compression that appear promising involve the discrete wavelet transform (DWT), after the pioneering work by Ingrid Daubechies in 1987 [Daubechies]. The reasons for this are that the DWT operates on the whole image as a single block thereby avoiding blocking artifacts typical in JPEG, while dynamically adjusting its spatial/frequency resolution to the appropriate level in various regions of the image.

A Harvard Medical School/Massachusetts General Hospital study using a range of medical xray image types including chest, bone and abdomen found DWT to provide reasonably high CRs with minimal degradation [Goldberg]. In this study, seven board-certified radiologists found no clinically relevant degradation in reconstructed images up to a CR of 40 with one exception, a subtle case of subperiosteal bone resorption.

Another study, done by Kodak for the National Image transfer Format standards committee, compared several lossy techniques applied to an image set that included a CT image, military recon images and the Lena test image. Wavelet transform was ranked much lower [Brower]. These and other studies strongly suggest that the wavelet transform is a promising approach.

METHODS

Our objective is to design and develop two systems, employing different approaches to redundancy reduction. Each will have multiple stages that themselves represent different compression techniques. Common to both systems is the first stage which is motivated by the observation that for many of the slices, the field of view (2048 x 1216 pixels) substantially exceeds the region of interest, the remaining area being uniform background. The approach therefore is to start with the removal of the background that surrounds the anatomic slices in each frame, and the representation of the remaining (significant) region as rectangle-encompassed slice data together with 44 bits of rectangle coordinate information. Beyond this common first stage, the two approaches are very different, one being lossless and the other lossy. In the lossless approach, the second stage is based on the recognition that a considerable degree of redundancy appears to exist on adjacent 2D slices. This allows the labeling of a relatively small percentage, say 10%, of the total number of slices as "reference" slices and the intervening ones as "difference" slices containing the pixel differences between adjacent frames. The third stage is to reduce the redundancy in the difference slices by runlength coding or other lossless technique. The advantage of this lossless or "information-preserving" approach is that it requires no expert evaluation of quality, since the quality of the compressed data will be identical to that of the original. The disadvantage is that compressibility is likely to be limited, the precise degree to be determined by experiment. Although conventional lossless methods yield CR up to 9 have been reported. An example is a technique applied to mammograms involving segmenting the breast and background, compressing the former with a predictive (lossless) coding method, and discarding the latter [Wang]. This is an approach relying on a preprocessing step similar to our image segmentation stage where the blue background in the slice images is removed.

The second (lossy) approach also starts with removal of the background resulting in rectangle-encompassed slice data. These slices are then subjected to data decorrelation by either JPEG or wavelet transform. As mentioned above, JPEG, an industry standard technique, has the advantage of commercial products that may be incorporated into the compression system, but has the disadvantage of blocky artifacts in the reconstructed images arising form the representation of each image as a collection of 8 x 8 pixel blocks prior to the application of discrete cosine transform for data decorrelation. Preliminary investigation in our lab reveals that blocky artifacts do exist in the reconstructed Visible Human images and are increasingly visible at higher CRs.

Our preliminary results and current research activity suggest that the wavelet transform will be a strong candidate for our design of a lossy compression scheme. DWT appears to allow relatively high CRs without the same degree of type of artifactual creation. Since relatively high CRs would be necessary for a practical and effective system to substantially improve the efficiency of storage and transmission of the Visible Human images, the investigation of wavelet transform applied to these images will continue. This technique applied to compression is still in an early stage of research, and has not yet been optimized for most image classes and definitely not for the Visible Human data, unique as this is in terms of texture complexity, color mapping and large size. An important initial objective is to select suitable filter coefficients, e.g. Daubechies 12, for the wavelet transformation. The transformation code employing these coefficients and suitable for 2048 x 1216 for each of the three color planes needs to be developed. Prior to applying this transformation, the 24 bit/pixel color images need to be separated in a preprocessing stage to 8 bit/pixel planes. Once the images are decorrelated and energy-compacted by the wavelet transform, they need to be quantized, the stage yielding the greatest degree of redundancy reduction. While uniform scalar quantization is simple to implement, vector quantization has been shown to outperform scalar quantization in terms of quantitative quality/distortion measures such as mean square error and peak signal-to-noise ratio. This is especially true when data is not completely decorrelated as in the three high frequence subbands resulting from each wavelet decomposition stage. There are several options in the selection of vector quantization technique though the LBG algorithm appears to be frequently used [Cosman, Linde, Mitra]. Finally, following the quantization state, further redundancy removal may be achieved by Huffman of runlength coding, both of which are information-preserving.

References

Brower B. NITF BWC Study Phase I Final Report. Eastman Kodak Co., May 1990.

Brower B. Low-bit rate image compression evaluations. Proc. SPIE, Orlando FL, April 1994.

Cosman PC, Gray RM, Vetterli M. Vector quantization of image subbands: A survey. IEEE Trans. Image Processing 1996; vol.5; no. 2: 202-25.

Daubechies I. Orthonormal bases of compactly supported wavelets. Comm. Pure Appl. Math. (1988); 41: 909-96.

Goldberg MA, et al. Application of wavelet compression to digitized radiographs. AJR 1994; 163: 463-468.

Linde Y, Buzo A, Gray RM. An algorithm for vector quantization design. IEEE Trans. Comm, Jan. 1980; COM-28: 84-95.

Mitra S, Pemmaraju S. Adaptive vector quantization using a ART- based neuro-fuzzy clustering algorithm. Invited paper at ICNN'96, June 1996, Washington, DC [to appear].

Pennebaker WB, Mitchell JL. JPEG: Still image data compression standard. New York: Van Nostrand Reinhold, 1993; 314.

Wang J, Huang HK. In-line structure lossless digital mammographic image compression. Proc. SPIE Medical Imaging '96 Conference (1996); Vol. 2707, Paper 48.