Search PubMed⌕ Search

Biomedical subjects

A H Rowberg

Publications and source records attributed to A H Rowberg.

At least 19 recordsLinked to original sources
PubMed1999

Seamless multiresolution display of portable wavelet-compressed images.

Image storage, display, and distribution have been difficult problems in radiology for many years. As improvements in technology have changed the nature of the storage and display media, demand for image portability, faster image acquisition, and flexible image distribution is driving the development of responsive systems. Technology, such as the wavelet-based multiresolution seamless image database (MrSID) portable image format (PIF), is enabling image management solutions that address the shifting "point-of-care." The MrSID PIF employs seamless, multiresolution technology, which allows the viewer to determine the size of the image to be viewed, as well as the position of the viewing area within the image dataset. In addition the MrSID PIF allows control of the compression ratio of decompressed images. This capability offers the advantage of very rapid image recall from storage devices and portability for rapid transmission and distribution using the internet or wide-area networks. For example, in teleradiology, the radiologist or other physician desiring to view images at a remote location has full flexibility in being able to choose a quick display of an overview image, a complete display of a full diagnostic quality image, or both without compromising communication bandwidth. The MrSID algorithm will satisfy Joint Photographic Experts Group (JPEG) 2000 standards, thereby being compatible with future versions of the Digital Imaging and Communications in Medicine (DICOM) standard for image data compression.

Algorithms↗
PubMed1999

Developing a framework for worldwide image communication.

The increasing mobility of the population and frequent changes in healthcare coverage, in both the government and private sectors, require integration of medical records not only longitudinally, but also across a variety of healthcare providers. Early in 1998, the federal government decided to solve this problem by constructing a framework for access to medical records by all of the government's health care facilities, called the Government Computer-Based Patient Record (GCPR). The government consortium chose a proposal by Litton PRC, a partnership of 11 companies with complementary areas of expertise. The framework is based on open systems, which use publicly available standards, and includes a Master Patient Information Locator that allows access to medical information from remote facilities, based on creating a unique identifier for each and every individual patient. PRC will use the Digital Imaging and Communications in Medicine (DICOM) imaging standard for radiology, supplemented by Health Level Seven (HL7).

Computer Communication Networks↗
PubMed1999

Application of the advanced communications technology satellite to teleradiology and real-time compressed ultrasound video telemedicine.

The authors have investigated the application of the NASA Advanced Communications Technology Satellite (ACTS) to teleradiology and telemedicine using the Jet Propulsion Laboratory (JPL)-developed ACTS Mobile Terminal (AMT) uplink. In this experiment, bidirectional 128, 256, and 384 kbps satellite links were established between the ACTS/AMT, the ACTS in geosynchronous orbit, and the downlink terrestrial terminal at JPL. A terrestrial Integrated Digital Services Network (ISDN) link was established from JPL to the University of Washington Department of Radiology to complete the bidirectional connection. Ultrasound video imagery was compressed in real-time using video codecs adhering to the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) Recommendation H.261. A 16 kbps in-band audio channel was used throughout. A five-point Likert scale was used to evaluate the quality of the compressed ultrasound imagery at the three transmission bandwidths (128, 256, and 384 kbps). The central question involved determination of the bandwidth requirements to provide sufficient spatial and contrast resolution for the remote visualization of fine- and low-contrast objects. The 384 kbps bandwidth resulted in only slight tiling artifact and fuzziness owing to the quantizer step size; however, these motion artifacts were rapidly resolved in time at this bandwidth. These experiments have demonstrated that real-time compressed ultrasound video imagery can be transmitted over multiple ISDN line bandwidth links with sufficient temporal, contrast, and spatial resolution for clinical diagnosis of multiple disease and pathology states to provide subspecialty consultation and educational at a distance.

Humans↗
PubMed1995

Use of data in a radiology information system for labeling computed radiographs: an interface to connect the two systems.

Computers have greatly facilitated the processing and storage of radiologic information. Manufacturers of radiology information systems (RISs) are gradually incorporating options for interfacing their products with other computers (e.g., hospital information systems). However, a growing need exists to also interface RISs with digital radiologic equipment so that images (e.g., computed radiographs of the chest and skeleton) are automatically labeled with identification data. Such connectivity would eliminate redundant work by technologists, decrease errors in the labeling of images, and increase the consistency of patients' data within a radiology department. Unfortunately, the rapid advances in digital technology, combined with the lack of a well-defined standard for the transfer of demographic information between dissimilar systems, have delayed development of these interfaces. We have developed a widely applicable method to automatically transfer patients' demographic data from an RIS to a commercially available computed radiography system. A personal computer is configured with inexpensive programmable telecommunications software to create an interactive gateway, which eliminates the need for redundant data entry (compared with entering data once on the RIS and again on the stand-alone computed radiography system), and thus also decreases errors in the labeling of images.

Local Area Networks↗
PubMed1995

Future opportunities using technical advantages in picture processing and data transfer.

In recent years there have been many significant developments in the ability to process radiographic pictures and transfer the images from one location to another, and we can now examine the application of computers in the radiographic evaluation of rheumatoid arthritis. Image management and communication systems can assist in providing better health care while lowering costs. Although this work is still in its infancy, the rapid growth in both hardware capability and software techniques will soon make these techniques available to physicians everywhere. The expanding capability in the commercial world is being embraced by the radiology department, and will soon allow rheumatologists to access images at greater distances, and do more with the images which become available.

Arthritis, Rheumatoid↗
PubMed1994

Displaying radiologic images on personal 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 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.

Algorithms↗
PubMed1994

Displaying radiologic images on personal computers: image storage and compression--Part 2.

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.

Algorithms↗
PubMed1994

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.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1994

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, that illustrates many of the principles discussed in this review series. The program is available for no charge by anonymous file transfer protocol (ftp).

Audiovisual Aids↗
PubMed1994

Evaluation of a combined two- and three-dimensional compression method using human visual characteristics to yield high-quality 10:1 compression of cranial computed tomography scans.

RATIONALE AND OBJECTIVES: The compression of cranial computed tomography scans was improved by using independent intra- and interframe compression techniques. METHODS: For intraframe compression, an image was decomposed into four subimages, one subimage was chosen as a reference subimage, and three of the subimages were predicted from the reference subimage. The prediction error was encoded with a classified vector quantizer (CVQ) based on human visual perception characteristics. Interframe redundancy is exploited by a displacement estimated interslice (DEI) algorithm that encodes the differences between reference subimages from adjacent slices. This combined DEI/CVQ method was subjectively evaluated by 13 radiologists under a blinded protocol, and was compared to the CVQ method alone, the DEI method alone, the original images, and to a standard intraframe discrete cosine transform (DCT) compression method. RESULTS: Only the combined DEI/CVQ method at 10:1 compression was not scored significantly different from the original images. At 15:1 compression, the DEI/CVQ method was scored significantly better than the 10:1 DCT and any other 15:1 compression methods. CONCLUSIONS: Compressed image quality is enhanced by exploiting inter- and intraframe redundancy, and by modeling some characteristics of human visual perception. The DEI/CVQ method is well-suited for progressive transmission, and thus, holds potential in teleradiology as well as picture archiving and communications systems.

Analysis of Variance↗
PubMed1994

Dr. Browse, a digital image file format Browser.

The emerging widespread adoption of the Digital Imaging Communications in Medicine (DICOM) standard will increase the demand for radiologic image transfer between radiologic image acquisition, archive, display and printing devices. Unfortunately, there are and will continue to be many devices that do not and will not support this standard, especially older radiologic equipment and devices from nonradiologic vendors. Determining the image file format characteristics of images from such equipment is often difficult, and done on an ad hoc basis. We have developed a software tool that assists users in determining the image file format parameters of unknown radiologic images.

Analog-Digital Conversion↗
PubMed1993

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.

Computer Graphics↗
PubMed1993

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

This is the third article of our series for radiologists and imaging scientists on displaying, manipulating, and analyzing radiologic images on personal computers. Part 1 of this article discusses image storage and reviews the basic concepts of information theory and image compression; part 2 will discuss specific methods of image compression. There are a wide variety of removable storage devices available to users who need to archive radiologic images on their personal computers. Tape drives have potentially very large storage capacity but slow performance. Removable SyQuest (SyQuest Technology, Femont, CA) and Bernoulli disks have near hard disk performance and can store from 100 to 150 Mbytes. Magneto-optical drives can store nearly 1 Gb on a 5.25" disk, with somewhat slower performance. Selecting the most appropriate storage solution requires a careful balance of the user's requirements, including performance, storage needs, cost and compatibility with other users. Despite the advances in low cost high capacity storage technology, image compression remains a crucial technology for modern diagnostic radiology because digital images require such large amounts of storage. Image compression is possible because radiologic images have relatively low entropy (high information content) compared with random noise. Image compression is classified as lossless (nondestructive) or lossy (destructive). Lossless image compression commonly achieve compression ratios of 1.5:1 to 3:1 (33% to 67%), whereas lossy compression can compresses images from 3:1 to 30:1 (67% to 97%).(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Storage Devices↗
PubMed1993

A new method for computed tomography image compression using adjacent slice data.

RATIONALE AND OBJECTIVES: The authors developed and subjectively evaluated an interslice compression algorithm that explores the redundancy among adjacent slices of an x-ray computed tomography (CT) scan. This algorithm has been compared to an intraslice compression algorithm based on the two-dimensional discrete cosine transform. METHODS: Nine x-ray CT head images from three patients were compressed with this interslice method at compression ratios of 5:1, 10:1, and 15:1. The same images were also compressed with the intraslice method at the same ratios. Six radiologists judged quality of randomly selected compressed and decompressed images compared to that of the originals. The evaluation data were analyzed statistically with the analysis of variance and Tukey's multiple comparison. Kappa-like statistics (Williams index and O'Connell and Dobson indexes) were also calculated to measure the agreement among readers beyond the amount expected by chance. RESULTS: The interslice coding algorithm showed significantly better quality than the intraslice method at significance level 0.05, even though there was no difference in the objective distortion measure (signal-to-noise ratio). Also, the quality of 10:1 compressed images with the interslice coding algorithm was not significantly different from that of the originals at level 0.05. While large variations in agreement occurred among readers, the overall agreement was statistically significant. CONCLUSIONS: By using adjacent slice information in compressing x-ray CT images, significantly better quality in compressed and decompressed images was achieved. While 10:1 compressed images with the interslice algorithm were not significantly different from the originals in quality at level 0.05, effect on diagnostic accuracy remains to be investigated.

Algorithms↗