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Over the last decade, a picture archiving and communications system (PACS) has been accepted by an increasing number of clinical organizations. Today, PACS is considered as an essential image management and productivity enhancement tool. Image compression could further increase the attractiveness of PACS by reducing the time and cost in image transmission and storage as long as 1) image quality is not degraded and 2) compression and decompression can be done fast and inexpensively. Compared to JPEG, JPEG 2000 is a new image compression standard that has been designed to provide improved image quality at the expense of increased computation. Typically, the decompression time has a direct impact on the overall response time taken to display images after they are requested by the radiologist or referring clinician. In this paper, we present a fast JPEG 2000 decoder running on a low-cost programmable processor. It can decode a losslessly compressed 2048 x 2048 CR image in 1.51 s. Using this kind of a decoder, performing JPEG 2000 decompression at the PACS display workstation right before images are displayed becomes viable. A response time of 2 s can be met with an effective transmission throughput between the central short-term archive and the workstation of 4.48 Mb/s in case of CT studies and 20.2 Mb/s for CR studies. We have found that JPEG 2000 decompression at the workstation is advantageous in that the desired response time can be obtained with slower communication channels compared to transmission of uncompressed images.

This is the fourth article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Classic image processing is divided into point, area, frame, and geometric processes. Point processes change image pixel values based on the value of the pixel of interest. Histogram equalization adjusts the pixel values in the image based on the distribution of pixel values. Area processes change the pixel of interest based on the values of the surrounding pixels, known as the neighborhood. Area processes using a convolution kernel are often used as image filters. Common convolution kernels include low-frequency, high-frequency, and edge-enhancement filters. Edge enhancement can be performed with convolution kernels such as shift and difference, gradient-directional and Laplacian filters, or with nonlinear methods such as Sobel's algorithm. Frame processes mathematically combine two or more images, often for noise reduction and background subtraction. Geometric processes alter the location of pixels within the image, but usually not the pixel values. Common radiologic applications of image processing include window width and window level adjustments (point process), adaptive histogram equalization (area process), unsharp masking (area process), computed radiography image processing (combined area and point processes), digital subtraction angiography (frame and geometric processes), region of interest analysis (area process), and image rotation (geometric process). As digital imaging becomes more widespread, radiologists need to understand the image processing that is fundamental to these modalities.

This is part 2 of our article on image storage and compression, the third article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Image compression is classified as lossless (nondestructive) or lossy (destructive). Common lossless compression algorithms include variable-length bit codes (Huffman codes and variants), dictionary-based compression (Lempel-Ziv variants), and arithmetic coding. Huffman codes and the Lempel-Ziv-Welch (LZW) algorithm are commonly used for image compression. All of these compression methods are enhanced if the image has been transformed into a differential image based on a differential pulse-code modulation (DPCM) algorithm. The LZW compression after the DPCM image transformation performed the best on our example images, and performed almost as well as the best of the three commercial compression programs tested. Lossy compression techniques are capable of much higher data compression, but reduced image quality and compression artifacts may be noticeable. Lossy compression is comprised of three steps: transformation, quantization, and coding. Two commonly used transformation methods are the discrete cosine transformation and discrete wavelet transformation. In both methods, most of the image information is contained in a relatively few of the transformation coefficients. The quantization step reduces many of the lower order coefficients to 0, which greatly improves the efficiency of the coding (compression) step. In fractal-based image compression, image patterns are stored as equations that can be reconstructed at different levels of resolution.

Most digital radiologic images have an extended contrast range of 9 to 13 bits, and are stored in memory and disk as 16-bit integers. Consequently, it is difficult to view such images on computers with 8-bit red-green-blue (RGB) graphic systems. Two approaches have traditionally been used: (1) perform a one-time conversion of the 16-bit image data to 8-bit gray-scale data, and then adjust the brightness and contrast of the image by manipulating the color palette (palette animation); and (2) use a software lookup table to interactively convert the 16-bit image data to 8-bit gray-scale values with different window width and window level parameters. The first method can adjust image appearance in real time, but some image features may not be visible because of the lack of access to the full contrast range of the image and any region of interest measurements may be inaccurate. The second method allows "windowing" and "leveling" through the full contrast range of the image, but there is a delay after each adjustment that some users may find objectionable. We describe a method that combines palette animation and the software lookup table conversion method that optimizes the changes in image contrast and brightness on computers with standard 8-bit RGB graphic hardware--the dual lookup table algorithm. This algorithm links changes in the window/level control to changes in image contrast and brightness via palette animation.(ABSTRACT TRUNCATED AT 250 WORDS)