Alan H. Rowberg

Statistical distributions of DCT coefficients and their application to an interframe compression algorithm for 3-D medical images

Displacement estimated interframe (DEI) coding, a coding scheme for 3-D medical image data sets such as X-ray computed tomography (CT) or magnetic resonance (MR) images, is presented. To take advantage of the correlation between contiguous slices, a displacement-compensated difference image based on the previous image is encoded. The best fitting distribution functions for the discrete cosine transform (DCT) coefficients obtained from displacement compensated difference images are determined and used in allocating bits and optimizing quantizers for the coefficients. The DEI scheme is compared with 2-D block discrete cosine transform (DCT) as well as a full-frame DCT using the bit allocation technique of S. Lo and H.K. Huang (1985). For X-ray CT head images, the present bit allocation and quantizer design, using an appropriate distribution model, resulted in a 13-dB improvement in the SNR compared to the full-frame DCT using the bit allocation technique. For an image set with 5-mm slice thickness, the DEI method gave about 5% improvement in the compression ratio on average and less blockiness at the same distortion. The performance gain increases to about 10% when the slice thickness decreases to 3 mm.

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.

Subjective evaluation of compressed image quality

Lossy data compression generates distortion or error on the reconstructed image and the distortion becomes visible as the compression ratio increases. Even at the same compression ratio, the distortion appears differently depending on the compression method used. Because of the nonlinearity of the human visual system and lossy data compression methods, we have evaluated subjectively the quality of medical images compressed with two different methods, an intraframe and interframe coding algorithms. The evaluated raw data were analyzed statistically to measure interrater reliability and reliability of an individual reader. Also, the analysis of variance was used to identify which compression method is better statistically, and from what compression ratio the quality of a compressed image is evaluated as poorer than that of the original. Nine x-ray CT head images from three patients were used as test cases. Six radiologists participated in reading the 99 images (some were duplicates) compressed at four different compression ratios, original, 5:1, 10:1, and 15:1. The six readers agree more than by chance alone and their agreement was statistically significant, but there were large variations among readers as well as within a reader. The displacement estimated interframe coding algorithm is significantly better in quality than that of the 2-D block DCT at significance level 0.05. Also, 10:1 compressed images with the interframe coding algorithm do not show any significant differences from the original at level 0.05.

A predictive classified vector quantizer and its subjective quality evaluation for X-ray CT images

The authors have developed a new classified vector quantizer (CVQ) using decomposition and prediction which does not need to store or transmit any side information. To obtain better quality in the compressed images, human visual perception characteristics are applied to the classification and bit allocation. This CVQ has been subjectively evaluated for a sequence of X-ray CT images and compared to a DCT coding method. Nine X-ray CT head images from three patients are compressed at 10:1 and 15:1 compression ratios and are evaluated by 13 radiologists. The evaluation data are analyzed statistically with analysis of variance and Tukeys multiple comparison. Even though there are large variations in judging image quality among readers, the proposed algorithm has shown significantly better quality than the DCT at a statistical, significance level of 0.05. Only an interframe CVQ can reproduce the quality of the originals at 10:1 compression at the same significance level. While the CVQ can reproduce compressed images that are not statistically different from the originals in quality, the effect on diagnostic accuracy remains to be investigated.

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.

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.

Preliminary experience with portable digital imaging for intensive care radiography.

A digital radiography system based on reusable, photostimulable phosphor technology was evaluated in approximately 3,500 portable chest radiographs of patients in an intensive care unit. The system functioned well in this application. No major problems were encountered in the visualization of tubes or catheters or in the detection of pneumothoraces. Assessment of fluid volume status or the presence of small pleural effusions, especially when these were bilateral, was initially somewhat difficult but became easier as investigators became familiar with the system. Radiologists were quicker than nonradiologists to accept the minimized two-on-one display format. Critical evaluation of the overall performance of digital systems such as this one is needed for a better definition of the systems strengths and weaknesses. Specifically, statistical analyses of the ability to detect disease states such as pneumothoraces, interstitial lung disease, lung nodules, and pleural abnormalities need to be performed.

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 education at a distance.

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.