Showing papers on "Digital mammography published in 1987"

Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography

Abstract: We have investigated the application of computer-based methods to the detection of microcalcifications in digital mammograms. The computer detection system is based on a difference-image technique in which a signal-suppressed image is subtracted from a signal-enhanced image to remove the structured background in a mammogram. Signal-extraction techniques adapted to the known physical characteristics of microcalcifications are then used to isolate microcalcifications from the remaining noise background. We employ Monte Carlo methods to generate simulated clusters of microcalcifications that are superimposed on normal mammographic backgrounds. This allows quantitative evaluation of detection accuracy of the computer method and the dependence of this accuracy on the physical characteristics of the microcalcifications. Our present computer method can achieve a true-positive cluster detection rate of approximately 80% at a false-positive detection rate of one cluster per image. The potential application of such a computer-aided system to mammographic interpretation is demonstrated by its ability to detect microcalcifications in clinical mammograms.

Digital Mammography: ROC Studies of the Effects of Pixel Size and Unsharp-Mask Filtering on the Detection of Subtle Microcalcifications

Abstract: We investigated the spatial resolution requirement and the effect of unsharp-mask filtering on the detectability of subtle microcalcifications in digital mammography. Digital images were obtained by digitizing conventional screen-film mammograms with a 0.1 X 0.1 mm2 pixel size, processed with unsharp masking, and then reconstituted on film with a Fuji image processing/simulation system (Fuji Photo Film Co., Tokyo, Japan). Twenty normal cases and 12 cases with subtle microcalcifications were included. Observer performance experiments were conducted to assess the detectability of subtle microcalcifications in the conventional, the unprocessed digital, and the unsharp-masked mammograms. The observer response data were evaluated using receiver operating characteristic (ROC) and LROC (ROC with localization) analyses. Our results indicate that digital mammograms obtained with 0.1 X 0.1 mm2 pixels provide lower detectability than the conventional screen-film mammograms. The detectability of microcalcifications in the digital mammograms is improved by unsharp-mask filtering; the processed mammograms still provide lower accuracy than the conventional mammograms, however, chiefly because of increased false-positive detection rates for the processed images at each subjective confidence level. Viewing unprocessed digital and unsharp-masked images in pairs resulted in approximately the same detectability as that obtained with the unsharp-masked images alone. However, this result may be influenced by the fact that the same limited viewing time was necessarily divided between the two images.

Scanned-projection digital mammography.

Abstract: The effectiveness of film–screen mammography is limited by tradeoffs between latitude and contrast, film granularity, and the need to increase dose when antiscatter methods are used. We are currently developing a scanned‐projection digital mammography (SPDM) system to overcome these limitations. The system consists of a pair of scanning slits, a high‐resolution x‐ray image intensifier tube, a linear photodiode array, and a digital display. The detective quantum efficiency of the SPDM system at spatial frequencies up to 3 cycles/mm is similar to that of mammographic film–screen combinations, but is lower at high frequencies. For low‐contrast objects as small as 0.1 mm in diameter, the signal‐to‐noise ratio is currently equal to that of optimally exposed mammographic film–screen images for equal dose to the breast and superior for regions which would be underexposed or overexposed on film. This is achieved by the use of a low‐noise detectorsystem, geometric magnification, and scatter elimination. Images of a contrast‐detail phantom and excised breast tissue illustrate the superior contrast sensitivity of SPDM.

Digital Slot Scan Mammography Using CCDs

Abstract: A desirable mammography imaging system should offer high spatial resolution, high sensitivity, a wide dynamic range and a means of limiting detected x-ray scatter. Significant scatter reduction can be obtained with a slot scan acquisition format with minimal attenuation of the primary beam. A potential receptor for digital mammography is comprised of a x-ray screen optically coupled directly to a CCD detector. The dynamic range of our current front-illuminated CCD is about 400 using factory supplied readout circuitry. The high spatial resolution of the CCD (22.4 1p/mm at Nyquist frequency) and the x-ray screen are reduced by poor optical contact at this time. X-rays which interact with the2CCD generate a moderate signal (25-30% of the optical signal) when a thin (22mg/cm 2 ) Gd 2 O 2 S:Tb screen is used. Image segments of a Kodak breast phantom were acquired with both the screen-CCD detector and the CCD detector alone. Substantial improvements in detector performance could be achieved by utilizing a back-illuminated CCD with a slow scan, correlated double sampled readout and a structured x-ray screen. The concept of direct optical coupling with structured screens can be implemented in high energy digital scanning systems.

Digital Mammography: Development Of A Computer-Aided System For Detection Of Microcalcifications

Abstract: One of the potential advantages of digital radiography is that it allows computerized methods to be applied in the analysis of image abnormalities. In this study, automated detection of microcalcifications in digital mammograms was investigated. We employed a spatial filtering technique to suppress the structured background in a mammogram while enhancing the microcalcifications. Signal-extraction techniques based on the physical characteristics of microcalcifications were then used to isolate clustered microcalcifications from the remaining noise background. To obtain test mammograms with known signal locations for evaluation of the detection accuracy of the computer method, we employed Monte Carlo techniques to generate simulated microcalcification clusters which were then superimposed on normal mammograms. The results indicate that the computer method can achieve a true-positive cluster detection rate of approximately 85% at a false-positive detection rate of one cluster per image for microcalcifications of average subtlety. Detection accuracy is expected to increase if the parameters of the image-processing and signal-extraction techniques are optimized further.