Thurman Gillespy

Displaying radiologic images on personal computers: Image storage and compression: Part 1

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.

Optimized algorithms for displaying 16-bit gray scale images on 8-bit computer graphic systems.

Most personal computers contain 8-bit graphic display hardware, whereas most medical gray scale images are stored at 16-bit per pixel integers. To display medical gray scale images on such computers, the 16-bit image data must be remapped into 8-bit gray scale images. This report presents the algorithms and computer code that allow very rapid 16-bit to 8-bit image data transformation. These algorithms are helpful in allowing personal computers with at least the performance of a Macintosh II (Apple Computer, Cupertino, CA) computer to function as low-end picture archiving communication systems or personal workstations.

Radiological images on personal computers: Introduction and fundamental principles of digital images

This series of articles will explore the issues related to displaying, manipulating, and analyzing radiological images on personal computers (PC). This first article discusses the digital image data file, standard PC graphic file formats, and various methods for importing radiological images into the PC.

Displaying radiologic images on personal computers: Practical applications and uses

This is the fifth and final article in our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers (PCs). There are many methods of transferring radiologic images into a PC, including transfer over a network, transfer from an imaging modality storage archive, using a frame grabber in the image display console, and digitizing a radiograph or 35-mm slide. Depending on the transfer method, the image file may be an extended gray-scale contrast, 16-bit raster file or an 8-bit PC graphics file. On the PC, the image can be viewed, analyzed, enhanced, and annotated. Some specific uses and applications include making 35-mm slides, printing images for publication, making posters and handouts, facsimile (fax) transmission to referring clinicians, converting radiologic images into medical illustrations, creating a digital teaching file, and using a network to disseminate teaching material. We are distributing a 16-bit image display and analysis program for Macintosh computers, Dr Razz, taht illustrates many of the principles discussed in this review series. The program is available for no charge by anonymous file transfer protocol (ftp).

Displaying radiologic images on personal computers

This is the second article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers (PCs). The first article discussed the digital image data file, standard PC graphic file formats, and various methods for importing radiologic images into the PC. This article discusses the hardware, software, and user interface issues related to displaying gray scale images on PCs. In particular, this segment focuses on the process of converting the digital image into gray shades on a color monitor. A method for displaying and interactively setting the window width and window level parameters of 16-bit radiologic images on PCs with standard red green blue graphic hardware is illustrated in a sample application.

Dual lookup table algorithm: An enhanced method of displaying 16-bit gray-scale images on 8-bit RGB graphic systems

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. The purpose of the algorithm is to use palette animation to mimic changes in image appearance performed by the software lookup table method after the window width and window level parameters have changed. The algorithm combines the advantages of both methods: rapid manipulation of image brightness and contrast by palette animation, and the ability to window and level on the full 16-bit image data using the software lookup table. This algorithm may be useful for applications that display 16-bit radiologic images on computers with standard 8-bit RGB graphic systems.

Displaying radiologic images on pesonal computers: Image processing and analysis

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 Sobels 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.

Publishing radiology educational material on the internet: Analysis of e-mail responses

W rE HAVE published a basic primer on the temporomandibular joint (TMJ), the TMJ Tutorial, on the World Wide Web (WWW) server of the Department of Radiology at the University of Washington (http://www.rad.washington.edu/Anatomy/ TMJ/TMJ.html). The tutorial includes basic information about the anatomy of the TMJ, the pathophysiology of TMJ dysfunction, and the arthrographic, computed tomography (CT), and magnetic resonance (MR) imaging appearance of the normal and dysfunctional joint. The tutorial originally was designed on a Macintosh computer (Apple Computer, Cupertino, CA) as a animated computer exhibit, l and was intended f o r a radiology audience. Many non-radiology persons, however, have found the tutorial on the internet and have sent us e-mail.

Optimized algorithm for adaptive histogram equalization

Adaptive histogram equalization (AHE) is a useful technique for expanding local contrast in medical images. The method is based on histogram equalization of each pixel based on a local NXN image region. If the number of pixels in the NXN local region is equal to the number of grayshades in the image, the equalized histogram can be directly constructed from the distribution histogram. This optimization may permit AHE to be performed on standard medical image display workstations.