We have evaluated the potential benefit of incorporating a temporal subtraction scheme with our previously developed bilateral subtraction technique for improving the sensitivity of computer-aided diagnosis in the detection of breast masses in mammography. Evaluation of a scheme that combines output from bilateral and temporal subtraction indicates the potential for improvement in sensitivity. We have also performed a preliminary comparison of two different methods for the registration of temporal images. In order to explore methods to reduce false-positive rates, the extracted features of the false positive detections obtained by the bilateral subtraction and temporal subtraction schemes were compared and found to have similar characteristics.

To investigate the performance of a computerized method for the automated detection of clustered microcalcifications in digitized mammograms from a variety of screening centers, the authors invited 118 radiologists to bring up to five mammograms to their scientific exhibit at the 1993 meeting of the Radiological Society of North America (RSNA). Forty-three mammograms from 14 sites were brought to the exhibit, where they were digitized and analyzed. Results of the analysis on the RSNA cases were compared with those obtained on a standard database of 39 mammograms collected from two centers. The performance of the detection algorithm on the RSNA images was lower than that achieved on the standard database, This lower performance was due in part to the higher fraction of very subtle clustered microcalcifications in the RSNA cases, as well as the apparent dependence of the algorithm on image characteristics (eg, contrast and noise), which varied from center to center, The authors conclude that the algorithm is robust and accurate enough to undergo clinical testing, When it is implemented clinically, the computerized scheme must be customized to the image characteristics at each specific screening center to obtain optimal performance.

Artificial neural networks have been applied to the differentiation of masses from false- positive detections in digital mammograms. A database of 110 pairs of digital mammograms containing a total of 102 masses (54 malignant, 48 benign) was utilized in this study. Three- hundred-two false positive regions were selected from these images to be used in the training of the artificial neural network. Over 90 features were calculated for both the true masses and the false positives. Features that showed the most separation, in a one-dimensional analysis, between true positives and false positives were selected for artificial neural network input. A three level feed-forward neural network was used with one input layer, one hidden layer and an output layer. By varying the structure and learning rate of the neural network an optimal structure was found. The performance of the ANN was evaluated by means of receiver operating characteristic (ROC) analysis and free-response receiver operating characteristic (FROC) analysis. Results from a round robin evaluation yielded an Az of 0.97 in the task of differentiating between masses and false-positive detections. In the future, multi-dimensional feature analysis will be performed to obtain the optimal performance using a combination of rule-based decision making along with artificial neural networks.

Spiculation is a primary sign of malignancy for masses detected by mammography. In this study, we developed a technique that analyzes patterns and quantifies the degree of spiculation present. Our current approach involves (1) automatic lesion extraction using region growing and (2) feature extraction using radial edge-gradient analysis. Two spiculation measures are obtained from an analysis of radial edge gradients. These measures are evaluated in four different neighborhoods about the extracted mammographic mass. The performance of each of the two measures of spiculation was tested on a database of 95 mammographic masses using ROC analysis that evaluates their individual ability to determine the likelihood of malignancy of a mass. The dependence of the performance of these measures on the choice of neighborhood was analyzed. We have found that it is only necessary to accurately extract an approximate outline of a mass lesion for the purposes of this analysis since the choice of a neighborhood that accommodates the thin spicules at the margin allows for the assessment of margin spiculation with the radial edge-gradient analysis technique. The two measures performed at their highest level when the surrounding periphery of the extracted region is used for feature extraction, yielding Az values of 0.83 and 0.85, respectively, for the determination of malignancy. These are similar to that achieved when a radiologist's ratings of spiculation (Az = 0.85) are used alone. The maximum value of one of the two spiculation measures (FWHM) from the four neighborhoods yielded an Az of 0.88 in the classification of mammographic mass lesions.

We are developing computer-aided diagnosis (CAD) schemes for the detection of clustered microcalcifications and masses in digital mammograms. Here, CAD refers to a diagnosis made by a radiologist who uses the computerized analyses of radiographic images as a 'second opinion'. The radiologist would make the final diagnostic decision. The aim of CAD is to improve diagnostic accuracy by reducing the number of missed diagnoses. In this preliminary evaluation, 30 clinical cases from December 1991 having a focal mammographic finding were analyzed.