American College of Cardiology/ European Society of Cardiology international study of angiographic data compression phase III. Measurement Of image quality differences at varying levels of data compression.
(9/1404)
OBJECTIVES: We sought to investigate up to which level of Joint Photographic Experts Group (JPEG) data compression the perceived image quality and the detection of diagnostic features remain equivalent to the quality and detectability found in uncompressed coronary angiograms. BACKGROUND: Digital coronary angiograms represent an enormous amount of data and therefore require costly computerized communication and archiving systems. Earlier studies on the viability of medical image compression were not fully conclusive. METHODS: Twenty-one raters evaluated sets of 91 cine runs. Uncompressed and compressed versions of the images were presented side by side on one monitor, and image quality differences were assessed on a scale featuring six scores. In addition, the raters had to detect pre-defined clinical features. Compression ratios (CR) were 6:1, 10:1 and 16:1. Statistical evaluation was based on descriptive statistics and on the equivalence t -test. Results At the lowest CR (CR 6:1), there was already a small (15%) increase in assigning the aesthetic quality score indicating "quality difference is barely discernible-the images are equivalent.' At CR 10:1 and CR 16:1, close to 10% and 55%, respectively, of the compressed images were rated to be "clearly degraded, but still adequate for clinical use' or worse. Concerning diagnostic features, at CR 10:1 and CR 16:1 the error rate was 9.6% and 13.1%, respectively, compared with 9% for the baseline error rate in uncompressed images. CONCLUSIONS: Compression at CR 6:1 provides equivalence with the original cine runs. If CR 16:1 were used, one would have to tolerate a significant increase in the diagnostic error rate over the baseline error rate. At CR 10:1, intermediate results were obtained. (+info)
Extraction of microcalcifications in digital mammograms using regional watershed.
(10/1404)
In this report, a novel technique is proposed for computer-aided automatic extraction of microcalcifications in a digital mammogram. First, the microcalcifications are detected by morphological filtering, followed by entropy-based thresholding. Next, the microcalcifications are segmented by computing regional watershed. The proposed automatic technique is designed to serve as a visual aid to radiologists. Its efficacy is demonstrated through experimental results. (+info)
A multiscale algorithm for segmenting calcifications from high-resolution mammographic specimen radiographs.
(11/1404)
We have developed a multiscale algorithm for segmenting breast calcifications from high-resolution specimen radiographs. The algorithm was evaluated using 152 mammographic regions of interest digitized at a 15-microm spatial resolution. The true-positive detection rate was approximately 97.4% with 0.67 false-positives per image, and the segmentation error of individual calcification particles was approximately 5%. The performance of the algorithm is highly satisfactory. (+info)
An investigation of the effects of mammographic acquisition parameters on a semiautomated quantitative measure of breast cancer risk.
(12/1404)
The aim of this work was to investigate the effect of mammographic acquisition parameter variations on the estimation of percent density (PD) produced by a particular semiautomated algorithm. The PD algorithm requires the user to specify a threshold pixel value segmenting breast tissue of greater and lesser density. A whole breast specimen was imaged using a variety of acquisition techniques, and the image data were processed as prescribed by the PD algorithm. PD estimates for all possible values of the user threshold were calculated for all the images. The image data were normalized so that PD varied between 30% and 80% over a fixed threshold range of 23, and a PD value of 50% was obtained for a threshold value of 195. PD differences between all the images and a baseline standard mammographic acquisition technique were calculated. We also estimated PD differences caused by small (3%) variations in operator selection of the threshold value. We found that the largest differences in PD involved changes in the density control of the mammography unit, and changes in the detector (either film type or computed radiography). The maximum PD differences due to technique were all less than 10%, with root-mean-square (RMS) variations less than 4%. PD differences due to operator variation were 24% (maximum) and 6.1% (RMS). These findings suggest that PD differences due to mammographic technique will be considerably less than those inherent to the technique, due to operator variation. All of these estimates are likely larger than differences seen in practice since optimization of the threshold by the operator was not considered in this analysis. (+info)
CADMIUM II: acquisition and representation of radiological knowledge for computerized decision support in mammography.
(13/1404)
CADMIUM II is a system for the interpretation of mammograms. A novel aspect of the system is that it combines symbolic reasoning with image processing, in contrast with most other approaches, which use only image processing and rely on artificial neural networks (ANNs) to classify mammograms. A problem of ANNs is that the advice they give cannot be traced back to communicable diagnostic inferences. Our approach is to provide advice based on explicit knowledge about the diagnostic process. To this end, we have conducted a knowledge elicitation study which looked at the descriptors used by expert radiologists when making diagnostic decisions about mammograms. The analysis of the radiologists' reports yielded a set of salient diagnostic features. These were used to inform the advice provided by the symbolic decision making component of CADMIUM II. (+info)
A Bayesian network for mammography.
(14/1404)
The interpretation of a mammogram and decisions based on it involve reasoning and management of uncertainty. The wide variation of training and practice among radiologists results in significant variability in screening performance with attendant cost and efficacy consequences. We have created a Bayesian belief network to integrate the findings on a mammogram, based on the standardized lexicon developed for mammography, the Breast Imaging Reporting And Data System (BI-RADS). Our goal in creating this network is to explore the probabilistic underpinnings of this lexicon as well as standardize mammographic decision-making to the level of expert knowledge. (+info)
Objectivity and accuracy of mammogram interpretation using the BI-RADS final assessment categories in 40- to 49-year-old women.
(15/1404)
To determine if use of the five final assessment categories of the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) improved objectivity or accuracy of mammographic evaluation in 40- to 49-year-old women, fifty mammograms of 40- to 49-year-old women that were obtained at a tertiary referral teaching hospital were classified according to those five final assessment categories. The mammograms were blinded to six American Osteopathic Board of Radiology-certified radiologists who were asked to classify each mammogram within the five final BI-RADS categories based on the mediolateral oblique and craniocaudal views presented. No history was allowed. Use of the BI-RADS five final assessment categories provided moderate interobserver objectivity, moderately high agreement among the radiologists' interpretation (reliability), and moderate accuracy of interpretation (validity) when compared to criterion. Moderate interobserver reliability and accuracy has been previously identified; however, no scientific review of the BI-RADS five final assessment categories in 40- to 49-year-old females was discovered in the current literature. No overall improvement of objectivity or accuracy was demonstrated using the five final assessment categories of the BI-RADS lexicon in 40- to 49-year-old women. (+info)
Computers in imaging and health care: now and in the future.
(16/1404)
Early picture archiving and communication systems (PACS) were characterized by the use of very expensive hardware devices, cumbersome display stations, duplication of database content, lack of interfaces to other clinical information systems, and immaturity in their understanding of the folder manager concepts and workflow reengineering. They were implemented historically at large academic medical centers by biomedical engineers and imaging informaticists. PACS were nonstandard, home-grown projects with mixed clinical acceptance. However, they clearly showed the great potential for PACS and filmless medical imaging. Filmless radiology is a reality today. The advent of efficient softcopy display of images provides a means for dealing with the ever-increasing number of studies and number of images per study. Computer power has increased, and archival storage cost has decreased to the extent that the economics of PACS is justifiable with respect to film. Network bandwidths have increased to allow large studies of many megabytes to arrive at display stations within seconds of examination completion. PACS vendors have recognized the need for efficient workflow and have built systems with intelligence in the management of patient data. Close integration with the hospital information system (HIS)-radiology information system (RIS) is critical for system functionality. Successful implementation of PACS requires integration or interoperation with hospital and radiology information systems. Besides the economic advantages, secure rapid access to all clinical information on patients, including imaging studies, anytime and anywhere, enhances the quality of patient care, although it is difficult to quantify. Medical image management systems are maturing, providing access outside of the radiology department to images and clinical information throughout the hospital or the enterprise via the Internet. Small and medium-sized community hospitals, private practices, and outpatient centers in rural areas will begin realizing the benefits of PACS already realized by the large tertiary care academic medical centers and research institutions. Hand-held devices and the Worldwide Web are going to change the way people communicate and do business. The impact on health care will be huge, including radiology. Computer-aided diagnosis, decision support tools, virtual imaging, and guidance systems will transform our practice as value-added applications utilizing the technologies pushed by PACS development efforts. Outcomes data and the electronic medical record (EMR) will drive our interactions with referring physicians and we expect the radiologist to become the informaticist, a new version of the medical management consultant. (+info)