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Purpose: To evaluate a computer-aided diagnosis multimodality intelligent workstation as an aid to radiologists in the interpretation of mammograms and breast sonograms., Materials and Methods: An institutional review board approved the protocol for an observer study with signed consent, as well as the retrospective use of the mammograms, sonograms, and clinical data with waiver of consent. The HIPAA-compliant observer study was conducted with five breast radiologists and five breast imaging fellows, all of whom gave confidence ratings and patient management decisions, both without and with the computer aid, for 97 lesions that were unknown to both the observers and the computer. The performance of each observer without and with the computer aid was quantified by using four performance measures: area under the receiver operating characteristic curve (A(z)) value, partial A(z) value, sensitivity, and specificity. The statistical significance of the differences in the performance measures without and with the computer aid was determined by using a two-tailed t test for paired data., Results: Use of the computer aid resulted in an improvement of the average performance of the 10 observers, as measured by means of a statistically significant increase in A(z) value (0.87-0.92; P < .001), partial A(z) value (0.47-0.68; P < .001), and sensitivity (0.88-0.93; P = .005). A statistically significant difference was not found in the specificity without and with the computer aid (0.66-0.69; P = .20)., Conclusion: Use of multimodality intelligent workstations can improve the performance of radiologists in the task of differentiating malignant and benign lesions at mammography and sonography., (RSNA, 2006)

Rationale and Objectives: To develop and evaluate a two-stage computerized method that first detects suspicious regions on ultrasound images, and subsequently distinguishes among different lesion types., Materials and Methods: The first stage of detecting potential lesions was based on expected lesion shape and margin characteristics. After the detection stage, all candidate lesions were classified by a Bayesian neural net based on computer-extracted lesion features. Two separate tasks were performed and evaluated at the classification stage: the first classification task was the distinction between all actual lesions and false-positive detections; the second classification task was the distinction between actual cancer and all other detected lesion candidates (including false-positive detections). The neural nets were trained on a database of 400 cases (757 images), consisting of complex cysts and benign and malignant lesions, and tested on an independent database of 458 cases (1,740 images including 578 normal images)., Results: In the distinction between all actual lesions and false-positive detections, Az values of 0.94 and 0.91 were obtained with the training and testing data sets, respectively. Sensitivity by patient of 90% at 0.45 false-positive detections per image was achieved for this detection-plus-classification scheme for the testing data set. Distinguishing cancer from all other detections (false-positives plus all benign lesions) proved to be more challenging, and Az values of 0.87 and 0.81 were obtained during training and testing, respectively. Sensitivity by patient of 100% at 0.43 false-positive malignancies per image was achieved in the detection and classification of cancerous lesions for the testing dataset., Conclusion: The results show promising performance of the computerized lesion detection and classification method, and indicate the potential of such a system for clinical breast ultrasound.

Rationale and Objectives: To investigate the potential usefulness of computer-aided diagnosis as a tool for radiologists in the characterization and classification of mass lesions on ultrasound., Materials and Methods: Previously, a computerized method for the automatic classification of breast lesions on ultrasound was developed. The computerized method includes automatic segmentation of the lesion from the ultrasound image background and automatic extraction of four features related to lesion shape, margin, texture, and posterior acoustic behavior. In this study, the effectiveness of the computer output as an aid to radiologists in their ability to distinguish between malignant and benign lesions, and in their patient management decisions in terms of biopsy recommendation are evaluated. Six expert mammographers and six radiologists in private practice at an institution accredited by the American Ultrasound Institute of Medicine participated in the study. Each observer first interpreted 25 training cases with feedback of biopsy results, and then interpreted 110 additional ultrasound cases without feedback. Simulating an actual clinical setting, the 110 cases were unknown to both the observers and the computer. During interpretation, observers gave their confidence that the lesion was malignant and also their patient management recommendation (biopsy or follow-up). The computer output was then displayed, and observers again gave their confidence that the lesion was malignant and theirpatient management recommendation. Statistical analyses included receiver operator characteristic analysis and Student t-test., Results: For the expert mammographers and for the community radiologists, the Az (area under the receiver operator characteristic curve) increased from 0.83 to 0.87 (P = .02) and from 0.80 to 0.84 (P = .04), respectively, when the computer aid was used in the interpretation of the ultrasound images. Also, the Az values for the community radiologists with aid and for the expert mammographers without aid are similar to the Az value for the computer alone (Az = 0.83)., Conclusion: Computer analysis of ultrasound images of breast lesions has been shown to improve the diagnostic accuracy of radiologists in the task of distinguishing between malignant and benign breast lesions and in recommending cases for biopsy.

Purpose: To evaluate the effectiveness of a computerized classification method as an aid to radiologists reviewing clinical mammograms for which the diagnoses were unknown to both the radiologists and the computer., Materials and Methods: Six mammographers and six community radiologists participated in an observer study. These 12 radiologists interpreted, with and without the computer aid, 110 cases that were unknown to both the 12 radiologist observers and the trained computer classification scheme. The radiologists' performances in differentiating between benign and malignant masses without and with the computer aid were evaluated with receiver operating characteristic (ROC) analysis. Two-tailed P values were calculated for the Student t test to indicate the statistical significance of the differences in performances with and without the computer aid., Results: When the computer aid was used, the average performance of the 12 radiologists improved, as indicated by an increase in the area under the ROC curve (A(z)) from 0.93 to 0.96 (P <.001), by an increase in partial area under the ROC curve ((0.90)A(')(z)) from 0.56 to 0.72 (P <.001), and by an increase in sensitivity from 94% to 98% (P =.022). No statistically significant difference in specificity was found between readings with and those without computer aid (Delta = -0.014; P =.46; 95% CI: -0.054, 0.026), where Delta is difference in specificity. When we analyzed results from the mammographers and community radiologists as separate groups, a larger improvement was demonstrated for the community radiologists., Conclusion: Computer-aided diagnosis can potentially help radiologists improve their diagnostic accuracy in the task of differentiating between benign and malignant masses seen on mammograms., (Copyright RSNA, 2002)

We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.

We present a computer-aided diagnosis (CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average Az value of 0.87 in the task of distinguishing malignant from benign lesions.

Purpose: To investigate the potential usefulness of special view mammograms in the computer-aided diagnosis of mammographic breast lesions., Materials and Methods: Previously, we developed a computerized method for the classification of mammographic mass lesions on standard-view mammograms, i.e., mediolateral oblique (MLO) view and/or cranial caudal (CC) views. In this study, we evaluate the performance of our computerized classification method on an independent database consisting of 70 cases (33 malignant and 37 benign cases), each having CC, MLO, and special view mammograms (spot compression or spot compression magnification views). The mass lesion identified in each of the three mammographic views was analyzed using our previously developed and trained computerized classification method. Performance in the task of distinguishing between malignant and benign lesions was evaluated using receiver operating characteristic analysis. On this independent database, we compared the performance of individual computer-extracted mammographic features, as well as the computer-estimated likelihood of malignancy, for the standard and special views., Results: Computerized analysis of special view mammograms alone in the task of distinguishing between malignant and benign lesions yielded an Az of 0.95, which is significantly higher (p < 0.005) than that obtained from the MLO and CC views (Az values of 0.78 and 0.75, respectively). Use of only the special views correctly classified 19 of 33 benign cases (a specificity of 58%) at 100% sensitivity, whereas use of the CC and MLO views alone correctly classified 4 and 8 of 33 benign cases (specificities of 12% and 24%, respectively). In addition, we found that the average computer output of the three views (Az of 0.95) yielded a significantly better performance than did the maximum computer output from the mammographic views., Conclusions: Computerized analysis of special view mammograms provides an improved prediction of the benign versus malignant status of mammographic mass lesions.

In this paper we present a computationally efficient segmentation algorithm for breast masses on sonography that is based on maximizing a utility function over partition margins defined through gray-value thresholding of a preprocessed image. The performance of the segmentation algorithm is evaluated on a database of 400 cases in two ways. Of the 400 cases, 124 were complex cysts, 182 were benign solid lesions, and 94 were malignant lesions. In the first evaluation, the computer-delineated margins were compared to manually delineated margins. At an overlap threshold of 0.40, the segmentation algorithm correctly delineated 94% of the lesions. In the second evaluation, the performance of our computer-aided diagnosis method on the computer-delineated margins was compared to the performance of our method on the manually delineated margins. Round robin evaluation yielded Az values of 0.90 and 0.87 on the manually delineated margins and the computer-delineated margins, respectively, in the task of distinguishing between malignant and nonmalignant lesions.

Rationale and Objectives: The purpose of this study was to evaluate the robustness of a computerized method developed for the classification of benign and malignant masses with respect to variations in both case mix and film digitization., Materials and Methods: The classification method included automated segmentation of mass regions, automated feature-extraction, and automated lesion characterization. The method was evaluated independently with a 110-case database consisting of 50 malignant and 60 benign cases. Mammograms were digitized twice with two different digitizers (Konica and Lumisys). Performance of the method in differentiating benign from malignant masses was evaluated with receiver operating characteristic (ROC) analysis. Effects of variations in both case mix and film digitization on performance of the method also were assessed., Results: Categorization of lesions as malignant or benign with an artificial neural network (or a hybrid) classifier achieved an area under the ROC curve, Az, value of 0.90 (0.94 for the hybrid) on the previous training database in a round-robin evaluation and Az values of 0.82 (0.81) and 0.81 (0.82) on the independent database for the Konica and Lumisys formats, respectively. These differences, however, were not statistically significant (P > .10)., Conclusion: The computerized method for the classification of lesions on mammograms was robust with respect to variations in case mix and film digitization.

The limitations of radiologists when interpreting mammogram examinations provides a reasonable, if not compelling, basis for application of computer techniques that have the potential to improve diagnostic performance. Computer algorithms, at their present state of development, show great promise for clinical use. It can be expected that such use will only improve as computer technology and computer methods continue to become more formidable. The eventual role of computers in mammographic detection and diagnosis has not been fully defined, but their effect on practice may one day be very significant.

Rationale and Objectives: The purpose of this study was to develop and evaluate a fully automated method that spatially registers anterior, posterior, and lateral ventilation/perfusion (V/Q) images with posteroanterior and lateral digital chest radiographs to retrospectively combine the physiologic information contained in the V/Q scans with the anatomic detail in the chest radiographs., Materials and Methods: Gray-level thresholding techniques were used to segment the aerated lung regions in the radiographic images. A variable-thresholding technique combined with an analysis of image noise was used to segment the adequately perfused or ventilated lung regions in the scintigraphic images. The physical dimensions of the segmented lung regions in images from both modalities were used to properly scale the radiographic images relative to the radionuclide images. Computer-determined locations of anatomic landmarks were then used to rotate and translate the images to achieve registration. Pairs of corresponding radionuclide and radiographic images were enhanced with color and then merged to create superimposed images., Results: Five observers used a five-point rating scale to subjectively evaluate four image combinations for each of 50 cases. Of these ratings, 95.5% reflected very good, good, or fair registration., Conclusion: The automated method for the registration of radionuclide lung scans with digital chest radiographs to produce images that combine functional and structural information should benefit nuclear medicine physicians and radiologists, who must visually correlate images that differ greatly in physical size, resolution properties, and information content.

Purpose: To determine the prevalence of spiculation in a large series of screening-detected breast cancers appearing as masses on mammograms and to assess the sensitivity of a computer-aided detection (CAD) algorithm that uses spiculation measures in the detection of such lesions., Materials and Methods: Six hundred seventy-seven consecutive cases of breast cancers detected as masses on mammograms were independently reviewed by three radiologists who determined if the lesions were spiculated. All cancers were then analyzed by the CAD system., Results: All three radiologists interpreted 375 (55%) of the 677 masses as being spiculated on at least one view. The CAD algorithm correctly marked 322 (86%) of the 375 clearly spiculated masses, with a mean of 0.24 additional mass mark per image. With a looser definition of spiculation, 585 (86%) of the 677 masses were called spiculated by at least one radiologist on one view. The algorithm correctly marked 464 (79%) of the 585 lesions that were spiculated or possibly spiculated., Conclusion: Spiculation was clearly present in a majority (55%) of consecutive screening-detected breast cancer masses found on mammograms in a large clinical trial. Incorporation of spiculation measures is, therefore, an important strategy in the detection of breast cancer with CAD. A present-generation CAD algorithm correctly identified a large proportion (86%) of spiculated breast cancers.

Purpose: To determine the false-negative rate in screening mammography, the capability of computer-aided detection (CAD) to identify these missed lesions, and whether or not CAD increases the radiologists' recall rate., Materials and Methods: All available screening mammograms that led to the detection of biopsy-proved cancer (n = 1,083) and the most recent corresponding prior mammograms (n = 427) were collected from 13 facilities. Panels of radiologists evaluated the retrospectively visible prior mammograms by means of blinded review. All mammograms were analyzed by a CAD system that marks features associated with cancer. The recall rates of 14 radiologists were prospectively measured before and after installation of the CAD system., Results: At retrospective review, 67% (286 of 427) of screening mammography-detected breast cancers were visible on the prior mammograms. At independent, blinded review by panels of radiologists, 27% (115 of 427) were interpreted as warranting recall on the basis of a statistical evaluation index; and the CAD system correctly marked 77% (89 of 115) of these cases. The original attending radiologists' sensitivity was 79% (427 of [427 + 115]). There was no statistically significant increase in the radiologists' recall rate when comparing the values before (8.3%) with those after (7.6%) installation of the CAD system., Conclusion: The original attending radiologists had a false-negative rate of 21% (115 of [427 + 115]). CAD prompting could have potentially helped reduce this false-negative rate by 77% (89 of 115) without an increase in the recall rate.

Objective: We developed a new method to distinguish between various interstitial lung diseases that uses an artificial neural network. This network is based on features extracted from chest radiographs and clinical parameters. The aim of our study was to evaluate the effect of the output from the artificial neural network on radiologists' diagnostic accuracy., Materials and Methods: The artificial neural network was designed to differentiate among 11 interstitial lung diseases using 10 clinical parameters and 16 radiologic findings. Thirty-three clinical cases (three cases for each lung disease) were selected. In the observer test, chest radiographs were viewed by eight radiologists (four attending physicians and four residents) with and without network output, which indicated the likelihood of each of the 11 possible diagnoses in each case. The radiologists' performance in distinguishing among the 11 interstitial lung diseases was evaluated by receiver operating characteristic (ROC) analysis with a continuous rating scale., Results: When chest radiographs were viewed in conjunction with network output, a statistically significant improvement in diagnostic accuracy was achieved (p < .0001). The average area under the ROC curve was .826 without network output and .911 with network output., Conclusion: An artificial neural network can provide a useful "second opinion" to assist radiologists in the differential diagnosis of interstitial lung disease using chest radiographs.

Rationale and Objectives: The authors evaluated the usefulness of artificial neural networks (ANNs) in the differential diagnosis of interstitial lung disease., Materials and Methods: The authors used three-layer, feed-forward ANNs with a back-propagation algorithm. The ANNs were designed to distinguish between 11 interstitial lung diseases on the basis of 10 clinical parameters and 16 radiologic findings extracted by chest radiologists. Thus, the ANNs consisted of 26 input units and 11 output units. One hundred fifty actual clinical cases, 110 cases from previously published articles, and 110 hypothetical cases were used for training and testing the ANNs by using a round-robin (or leave-one-out) technique. ANN performance was evaluated with receiver operating characteristic (ROC) analysis., Results: The Az (area under the ROC curve) obtained with actual clinical cases was 0.947, and both the sensitivity and specificity of the ANNs were approximately 90% in terms of indicating the correct diagnosis with the two largest output values among the 11 diseases., Conclusion: ANNs using clinical parameters and radiologic findings may be useful for making the differential diagnosis of interstitial lung disease on chest radiographs.

Rationale and Objectives: To develop a method for differentiating malignant from benign masses in which a computer automatically extracts lesion features and merges them into an estimated likelihood of malignancy., Materials and Methods: Ninety-five mammograms depicting masses in 65 patients were digitized. Various features related to the margin and density of each mass were extracted automatically from the neighborhoods of the computer-identified mass regions. Selected features were merged into an estimated likelihood of malignancy by, using three different automated classifiers. The performance of the three classifiers in distinguishing between benign and malignant masses was evaluated by receiver operating characteristic analysis and compared with the performance of an experienced mammographer and that of five less experienced mammographers., Results: Our computer classification scheme yielded an area under the receiver operating characteristic curve (Az) value of 0.94, which was similar to that for an experienced mammographer (Az = 0.91) and was statistically significantly higher than the average performance of the radiologists with less mammographic experience (Az = 0.81) (P = .013). With the database used, the computer scheme achieved, at 100% sensitivity, a positive predictive value of 83%, which was 12% higher than that for the performance of the experienced mammographer and 21% higher than that for the average performance of the less experienced mammographers (P < .0001)., Conclusion: Automated computerized classification schemes may be useful in helping radiologists distinguish between benign and malignant masses and thus reducing the number of unnecessary biopsies.

Image quality considerations in medical radiography are as diverse and complex as are the types of anatomy and pathologic conditions encountered in clinical practice. Nevertheless, certain basic concepts are central to the discussion of image quality in any radiographic examination. These concepts include the types of significant, or target, findings that are expected to occur and the anatomic background on which they are likely to appear. Physical parameters of radiographic systems, such as contrast, sharpness, and noise, act in unison in determining the final appearance of a radiograph and affect not only the portrayal of the expected pathologic condition but also that of the normal anatomy. Basic radiographic approaches in different clinical radiographic examinations can be derived from anticipated targets and backgrounds as well as from known physical determinants of image quality in radiography.

Rationale and Objectives: The authors have developed an automated computerized technique for registering radionuclide lung scan images with digital chest radiographs., Methods: Threshold analysis was used to construct contours around the high-activity regions of radionuclide ventilation-perfusion images. Analogous contours were constructed around the lung regions of the corresponding digitized radiographs. Contour dimensions and anatomic landmark locations were then used to superimpose the radiographic, ventilation, and perfusion images., Results: Evaluation of 25 sets of images indicated that the scheme provided adequate to excellent registration in 91% of the pairwise combinations., Conclusion: This automated scheme for registering ventilation-perfusion images with digital chest radiographs has the potential to aid radiologists in the interpretation of these images.

Purpose: To develop a method for differentiating malignant from benign clustered microcalcifications in which image features are both extracted and analyzed by a computer., Materials and Methods: One hundred mammograms from 53 patients who had undergone biopsy for suspicious clustered microcalcifications were analyzed by a computer. Eight computer-extracted features of clustered microcalcifications were merged by an artificial neural network. Human input was limited to initial identification of the microcalcifications., Results: Computer analysis allowed identification of 100% of the patients with breast cancer and 82% of the patients with benign conditions. The accuracy of computer analysis was statistically significantly better than that of five radiologists (P = .03)., Conclusion: Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.

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.

A computer-aided diagnosis scheme to assist radiologists in detecting clustered microcalcifications from mammograms is being developed. Starting with a digital mammogram, the scheme consists of three steps. First, the image is filtered so that the signal-to-noise ratio of microcalcifications is increased by suppression of the normal background structure of the breast. Secondly, potential microcalcifications are extracted from the filtered image with a series of three different techniques: a global thresholding based on the grey-level histogram of the full filtered image, an erosion operator for eliminating very small signals, and a local adaptive grey-level thresholding. Thirdly, some false-positive signals are eliminated by means of a texture analysis technique, and a non-linear clustering algorithm is then used for grouping the remaining signals. With this method, the scheme can detect approximately 85% of true clusters, with an average of two false clusters detected per image.

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.

Although general rules for the differentiation between benign and malignant breast lesions exist, only 10 to 20% of masses referred for surgical breast biopsy are actually malignant. We are developing, as an aid to radiologists, a computerized scheme for the classification of masses appearing on mammograms to reduce the number of false-positive diagnoses of malignancies. The classification scheme involves the extraction of the margin of masses in order to quantify the degree of spiculation, which, in turn, is related to the likelihood of malignancy. When two measures of spiculation are used as input to an artificial neural network, the scheme achieves a performance similar to that achieved when radiologist's spiculation ratings alone are used for a clinical database of 53 masses. The computerized classification scheme therefore has the potential to effectively aid radiologists in determining appropriate patient management.

The revolution in digital computer technology that has made possible new and sophisticated imaging techniques may next influence the interpretation of radiologic images. In mammography, computer vision and artificial intelligence techniques have been used successfully to detect or to characterize abnormalities on digital images. Radiologists supplied with this information often perform better at mammographic detection or characterization tasks in observer studies than do unaided radiologists. This technology therefore could decrease errors in mammographic interpretation that continue to plague human observers.

An automated technique for the alignment of right and left breast images has been developed for use in the computerized analysis of bilateral breast images. In this technique, the breast region is first identified in each digital mammogram by use of histogram analysis and morphological filtering operations. The anterior portions of the tracked breast border and computer-identified nipple positions are selected as landmarks for use in image registration. The paired right and left breast images, either from mediolateral oblique or craniocaudal views, are then registered relative to each other by use of a least-squares matching method. This automated alignment technique has been applied to our computerized detection scheme that employs a nonlinear bilateral-subtraction method for the initial identification of possible masses. The effectiveness of using bilateral subtraction in identifying asymmetries between corresponding right and left breast images is examined by comparing detection performances obtained with various computer-simulated misalignments of 40 pairs of clinical mammograms. Based on free-response receiver operating characteristic and regression analyses, the detection performance obtained with the automated alignment technique was found to be higher than that obtained with simulated misalignments. Detection performance decreased gradually as the amount of simulated misalignment increased. These results indicate that automatic alignment of breast images is possible and that mass-detection performance appears to improve with the inclusion of asymmetric anatomic information but is not sensitive to slight misalignment.

Mammographic screening of asymptomatic women has shown effectiveness in the reduction of breast cancer mortality. We are developing a computerized scheme for the detection of mammographic masses as an aid to radiologists in mammographic screening programs. Possible masses on digitized screen/film mammograms are initially identified using a nonlinear bilateral-subtraction technique, which is based on asymmetric density patterns occurring in corresponding portions of right and left mammograms. In this study, we analyze the characteristics of actual masses and nonmass detections to develop feature-analysis techniques with which to reduce the number of nonmass (ie, false-positive) detections. These feature-analysis techniques involve (1) the extraction of various features (such as area, contrast, circularity and border-distance based on the density and geometric information of masses in both processed, and original breast images), and (2) tests of the extracted features to reduce nonmass detections. Cumulative histograms of both actual-mass detections and nonmass detections are used to characterize extracted features and to determine the cutoff values used in the feature tests. The effectiveness of the feature-analysis techniques is evaluated in combination with the computerized detection scheme that uses the nonlinear bilateral-subtraction technique using free-response receiver operating characteristic analysis and 77 patient cases (308 mammograms). Results show that the feature-analysis techniques effectively improve the performance of the computerized detection scheme: about 35% false-positive detections were eliminated without loss in sensitivity when the feature-analysis techniques were used.

The choice of clinical cases used to train and test a computer-aided diagnosis (CAD) scheme can affect the test results (i.e., error rate). In this study, we deliberately modified the components of our testing database to study the effects of this modification on measured performance. Using a computerized scheme for the automated detection of breast masses from mammograms, it was found that the sensitivity of the scheme ranged between 26% and 100% (at a false positive rate of 1.0 per image) depending on the cases used to test the scheme. Even a 20% change in the cases comprising the database can reduce the measured sensitivity by 15%-25%. Because of the strong dependence of measured performance on the testing database, it is difficult to estimate reliably the accuracy of a CAD scheme. Furthermore, it is questionable to compare different CAD schemes when different cases are used for testing. Sharing databases, creating a common database, or using a quantitative measure to characterize databases are possible solutions to this problem. However, none of these solutions exists or is practiced at present. Therefore, as a short-term solution, it is recommended that the method used for selecting cases, and histograms or mean and standard deviations of relevant image features be reported whenever performance data are presented.

A computerized scheme for the automated detection of clustered microcalcifications from digital mammograms is being developed. This scheme is one part of an overall package for computer-aided diagnosis (CAD), the purpose of which is to assist radiologists in detecting and diagnosing breast cancer. One important step in the computer detection scheme is to group or cluster microcalcifications, since clustered microcalcifications are more clinically significant than are isolated microcalcifications. Previously a "growing" technique in which signals (possible microcalcifications) were clustered by grouping those that were within some predefined distance from the center of the growing cluster was used. In this paper, a new technique for grouping signals, which consists of two steps, is introduced. First, signals that may be several pixels in area are reduced to single pixels by means of a recursive transformation. Second, the number of signals (nonzero pixels) within a small region, typically 3.2 x 3.2 mm, are counted. Only if three or more signals are present within such a region are they preserved in the output image. In this way, isolated signals are eliminated. Furthermore, this method can eliminate falsely detected clusters, which were identified by a previous detection scheme, based on the spatial distribution of signals within the cluster. The differences in performance of the CAD scheme for detecting clustered microcalcifications using the old and new clustering techniques was measured using 78 mammograms, containing 41 clusters.(ABSTRACT TRUNCATED AT 250 WORDS)

Rationale and Objectives: Identification of regions as possible masses on digitized screen film mammograms is an important initial step in the computerized detection of breast carcinomas. Possible masses may be initially extracted using criteria based on optical densities, geometric patterns, and asymmetries between corresponding locations in right and left mammograms. In this study, the usefulness of information arising from mammographic asymmetries for the identification of mass lesions is investigated., Methods: Two techniques are investigated--a nonlinear bilateral-subtraction technique based on image pairs and a local gray-level thresholding technique based on single images. Detection performances obtained with the two techniques in combination with various feature-analysis techniques are evaluated using 154 pairs of mammograms and compared using free-response receiver operating characteristic (FROC) analysis., Results: The nonlinear bilateral-subtraction technique performed better than the local gray-level thresholding technique., Conclusion: The incorporation of asymmetric information appears to be useful for computerized identification of possible masses on mammograms.

Computer-aided diagnosis (CAD) involves a computerized analysis of radiographs that is used as a "second opinion" by the radiologist. The approach presented incorporates computer vision and artificial intelligence techniques and includes schemes for the analysis of lung nodules, interstitial infiltrates, and cardiomegaly seen on chest radiographs; masses and clustered microcalcifications on mammograms; and stenoses and blood flow on angiograms. The demonstration of various CAD schemes in chest radiography and mammography on a six-monitor workstation simulates one possible clinical implementation of CAD in radiology. Whether soft- or hard-copy display media are used, the radiologist can refer to the CAD results and still use the original radiograph for the final diagnosis. Although initial impressions of this simulated "intelligent" workstation are encouraging, CAD is still in a preliminary stage of development. Various methods for effectively and efficiently integrating CAD into a clinical radiology department are being investigated.

The authors investigated the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammographic data. Three-layer, feed-forward neural networks with a back-propagation algorithm were trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists. A network that used 43 image features performed well in distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve for textbook cases in a test with the round-robin method. With clinical cases, the performance of a neural network in merging 14 radiologist-extracted features of lesions to distinguish between benign and malignant lesions was found to be higher than the average performance of attending and resident radiologists alone (without the aid of a neural network). The authors conclude that such networks may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.

A computerized scheme is being developed for the detection of masses in digital mammograms. Based on the deviation from the normal architectural symmetry of the right and left breasts, a bilateral subtraction technique is used to enhance the conspicuity of possible masses. The scheme employs two pairs of conventional screen-film mammograms (the right and left mediolateral oblique views and craniocaudal views), which are digitized by a TV camera/Gould digitizer. The right and left breast images in each pair are aligned manually during digitization. A nonlinear bilateral subtraction technique that involves linking multiple subtracted images has been investigated and compared to a simple linear subtraction method. Various feature-extraction techniques are used to reduce false-positive detections resulting from the bilateral subtraction. The scheme has been evaluated using 46 pairs of clinical mammograms and was found to yield a 95% true-positive rate at an average of three false-positive detections per image. This preliminary study indicates that the scheme is potentially useful as an aid to radiologists in the interpretation of screening mammograms.

Relatively simple, but important, detection tasks in radiology are nearing accessibility to computer-aided diagnostic (CAD) methods. The authors have studied one such task, the detection of clustered microcalcifications on mammograms, to determine whether CAD can improve radiologists' performance under controlled but generally realistic circumstances. The results of their receiver operating characteristic (ROC) study show that CAD, as implemented by their computer code in its present state of development, does significantly improve radiologists' accuracy in detecting clustered microcalcifications under conditions that simulate the rapid interpretation of screening mammograms. The results suggest also that a reduction in the computer's false-positive rate will further improve radiologists' diagnostic accuracy, although the improvement falls short of statistical significance in this study.

When characteristic x-rays are generated and reabsorbed in the phosphor of a radiographic screen-film system, the positions at which light is emitted from the initial and secondary interactions are correlated. A simple statistical model is developed to account for the effect of this correlation on the Wiener spectrum of quantum mottle. Unlike previous models, which ignore spatial correlation, the new model predicts that not only noise magnitude but also noise texture is changed as the incident x-ray energy exceeds the phosphor K-edge.

The mammographic examination can be considered from many different perspectives, not the least of which include the complex diagnostic or public health issues that determine the place of this study in modern medical practice. There is, however, no finer example than mammography of the role of radiological science in radiography. It is important that radiologists remain ever cognizant of this role in order to maximize the benefit of the examination to their patients.

Dependence of the speed of various screen/film systems on x-ray energy was studied using the nearly monoenergetic x rays emitted by a filtered fluorescent source. The results show that response depends on screen phosphor composition and thickness. Barium and rare earth screens having K absorption energies lower than that of calcium tungstate are relatively more sensitive to x rays in the 40-70-keV region.

Charged-particle bombardment of thick beryllium targets produces a neutron yield varying with angle, and an isotropic gamma component. Differences in detector response in such a field are due to neutrons alone. With accurate neutron spectral distributions and measurements of detector response, a computer code can be used to determine the neutron sensitivity of the detector as a function of energy.