Computer-aided detection of clustered microcalcifications in digitized mammograms

A New Approach to the Detection of Lesions in Mammography Using Fuzzy Clustering

Breast cancer is a leading cause of female mortality and its early detection is an important means of reducing this. The present study investigated an approach, based on fuzzy clustering, to detect small lesions, such as microcalcifications and other masses, that are hard to recognize in breast cancer screening. A total of 180 mammograms were analysed and classified by radiologists into three groups ( n = 60 per group): those with microcalcifications; those with tumours; and those with no lesions. Twenty mammograms were taken as training data sets from each of the groups. The algorithm was then applied to the data not taken for training. Analysis by fuzzy clustering achieved a mean accuracy of 99.7% compared with the radiologists' findings. It was concluded that the fuzzy clustering algorithm allowed for more efficient and accurate detection of breast lesions and may improve the early detection of breast tumours.

Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.

Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques

In this work, the effect of an image enhancement processing stage and the parameter tuning of a computer-aided detection (CAD) system for the detection of microcalcifications in mammograms is assessed. Five (5) image enhancement algorithms were tested introducing the contrast-limited adaptive histogram equalization (CLAHE), the local range modification (LRM) and the redundant discrete wavelet (RDW) linear stretching and shrinkage algorithms. CAD tuning optimization was targeted to the percentage of the most contrasted pixels and the size of the minimum detectable object which could satisfactorily represent a microcalcification. The highest performance in two mammographic datasets, were achieved for LRM (A(Z)=0.932) and the wavelet-based linear stretching (A(Z)=0.926) methodology.

Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method

RATIONALE AND OBJECTIVES

The free-response paradigm is being increasingly used in the assessment of medical imaging systems. The currently implemented method of analyzing the data, namely jackknife free-response (JAFROC) analysis, has some validation and applicability limitations. The purpose of this work is to address these limitations.

MATERIALS AND METHODS

The general principles of modality evaluation and methodology validation are reviewed. A model for simulating free-response data was used to test the statistical validity of several methods of analyzing the data. The methods differed only in the choice of the figure of merit used to quantify performance. Statistical validity was judged by investigating the behaviors of the methods under null hypothesis conditions of no difference between modalities.

RESULTS

The validity of the different methods of analyzing the data was found to be dependent on the choice of figure of merit. A figure of merit is identified that accommodates abnormal images with multiple (one or more) lesions, detections of which could have different clinical significances (weights). This figure of merit is shown to be statistically valid. An extension of the analysis to single reader interpretations of images from different modalities is also shown to be statistically valid.

CONCLUSION

With the validated enhancements, JAFROC is expected to be of greater utility to users of the free-response method. The extension to single-reader interpretations should be of particular value to developers of image processing algorithms, including developers of computer-aided diagnosis algorithms.

Impact of breast density on computer-aided detection in full-field digital mammography

The goal of this study was to evaluate the performance of a computer-aided detection (CAD) system in full-field digital mammography (Senographe 2000D, General Electric, Buc, France) in finding out carcinomas depending on the parenchymal density. A total of 226 mediolateral oblique (MLO) and 186 craniocaudal (CC) mammographic views of histologically proven cancers were retrospectively evaluated with a digital CAD system (ImageChecker V2.3 R2 Technology, Los Altos, CA, USA). Malignant tumors were detected correctly by CAD in MLO view in 84.85% in breasts with parenchymal tissue density of the American College of Radiology (ACR) type 1, in 70.33% of the ACR type 2, in 68.12% of the ACR type 3, and in 69.70% of the ACR type 4. For the CC view, similar results were found according to the ACR types. Using the chi-square and McNemar tests, there was no statistical significance. However, a trend of better detection could be seen with decreasing ACR type. In conclusion, there seems to be a tendency for breast tissue density to affect the detection rate of breast cancer when using the CAD system.

Computer aided detection of clusters of microcalcifications on full field digital mammograms

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from the artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.

Computer-aided detection in full-field digital mammography: detection in dependence of the BI-RADS categories

The object of this study was to determine the performance of a computer-aided detection system in full-field digital mammography (Senographe 2000D, General Electric, Buc, France) in detecting carcinomas in breasts in dependence of the initial Breast Imaging Reporting and Data System (BI-RADS) categories. A total of 226 mediolateral oblique (MLO) and 186 craniocaudal (CC) view mammograms of histologically proven cancers were retrospectively evaluated with a primary digital computer-aided detection system (Image Checker V2.3; R2 Technology, Los Altos, CA). According to BI-RADS of the American College of Radiology (ACR), the lesions were classified in MLO view as BI-RADS 1 in 2 cases, BI-RADS 2 in 11 cases, BI-RADS 3 in 37 cases, BI-RADS 4 in 56 cases, and BI-RADS 5 in 120 cases, and in CC view as BI-RADS 1 in 2 cases, BI-RADS 2 in 8 cases, BI-RADS 3 in 26 cases, BI-RADS 4 in 46 cases, and BI-RADS 5 in 104 cases. The computer-aided detection system shows markers also in mammograms classified as BI-RADS categories 1-3 by the radiologist. Furthermore, BI-RADS categories 4 and 5 in most cases demonstrate masses in mammography. With increasing BI-RADS category, the computer-aided detection system shows decreasing numbers of overlooked carcinomas. In MLO view, no markers were found in 100% (2/2), 81.8% (9/11), 59.5% (22/37), 46.4% (26/56), and 15% (18/120) for BI-RADS categories 1-5, respectively. False-positive markers, however, were seen in 0.5 per image (205/412). In conclusion, the high rate of false-positive markers shows that the specificity of the computer-aided detection system is limited and that improvements are necessary.

CAD for mammography: the technique, results, current role and further developments

CAD systems, developed to assist the radiologist in the detection of suspicious lesions on mammograms, are currently controversially discussed. The highly sensitive detection of malignant structures including priors by CAD is linked with a low specific performance and a high rate of falsely positive markings. This causes controversial results regarding the effect of CAD systems for the diagnosing radiologist. This review aims to give an overview of the current literature, to state the currently discussed controversial results of CAD and to give an outlook on the next developments, which are not limited to senology, but include many other applications of CAD systems in radiology.

Effect of breast density on computer aided detection

PURPOSE

This study was conducted to assess the clinical impact of breast density and density of the lesion's background on the performance of a computer-aided detection (CAD) system in the detection of breast masses (MA) and microcalcifications (MC).

MATERIALS AND METHODS

A total of 200 screening mammograms interpreted as BI-RADS 1 and suspicious mammograms of 150 patients having a histologically verified malignancy from 1992 to 2000 were selected by using a sampler of tumor cases. Excluding those cases having more than one lesion or a contralateral malignancy attributable to statistical reasons, 127 cases with 127 malignant findings were analyzed with a CAD system (Second Look 5.0, CADx Systems, Inc., Beavercreek, OH). Of the 127 malignant lesions, 56 presented as MC and 101 presented as MA, including 30 cases with both malignant signs. Overall breast density of the mammogram and density of the lesion's background were determined by two observers in congruence (density a: entirely fatty, density b: scattered fibroglandular tissue, density c: heterogeneously dense, density d: extremely dense).

RESULTS

Within the unsuspicious group, 100/200 cases did not have any CAD MA marks and were therefore truly negative (specificity 50%), and 151/200 cases did not have any CAD MC marks (specificity 75.5%). For these 200 cases, the numbers of marks per image were 0.41 and 0.37 (density a), 0.38 and 0.97 (density b), 0.44 and 0.91 (density c), and 0.58 and 0.68 (density d) for MC and MA marks, respectively (Fisher's t-test: n.s. for MC, p < 0.05 for MA). Malignant lesions were correctly detected in at least one view by the CAD system for 52/56 (92.8%) MC and 91/101 (90.1%) MA. Detection rate versus breast density was: 4/6 (66.7%) and 18/19 (94.7%) (density a), 32/33 (97.0%) and 49/51 (96.1%) (density b), 14/15 (93.3%) and 23/28 (82.1%) (density c), and 2/2 (100%) and 1/3 (33.3%) (density d) for MC and MA, respectively. Detection rate versus the lesion's background was: 19/21 (90.5%) and 36/38 (94.7%) (density a), 34/36 (94.4%) and 59/62 (95.2%) (density b), 8/9 (88.9%) and 20/24 (83.3%) (density c), and 9/10 (90%) and 4/8 (50%) (density d) for groups 2 and 3, respectively. Detection rates differed significantly for masses in heterogeneously dense and extremely dense tissue (overall or lesion's background) versus all other densities (Fisher's t-test: p < 0.05). A significantly lowered FP rate for masses was found on mammograms of entirely fatty tissue.

CONCLUSION

Overall breast density and density at a lesion's background do not appear to have a significant effect on CAD sensitivity or specificity for MC. CAD sensitivity for MA may be lowered in cases with heterogeneously and extremely dense breasts, and CAD specificity for MA is highest in cases with extremely fatty breasts. The effects of overall breast density and density of a lesion's background appear to be similar.

An Evaluation of Contrast Enhancement Techniques for Mammographic Breast Masses

The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.

Are unnecessary follow-up procedures induced by computer-aided diagnosis (CAD) in mammography? Comparison of mammographic diagnosis with and without use of CAD

OBJECTIVE

To evaluate the rate of unnecessary follow-up procedures recommended by radiologists using a CAD-system.

MATERIALS AND METHODS

185 patients (740 images) were consecutively selected from three groups (36 histologically proven cancers = group 1; 49 histologically proven benign lesions = group 2 and 100 screening cases (4 years-follow up = group 3). Mammograms were evaluated by a CAD system (Second Look, CADx, Canada). Five blinded radiologists assessed the images without/with CAD outputs. Diagnostic decisions were ranked from surely benign to surely malignant according to BIRADS classification, follow-up procedures were recommended for each observed lesion (a, screening; b, short interval follow-up examination in 6 months; c, pathologic clarification).

RESULTS

CAD-system detected 32/36 cancers (88.9%) (FP-rate: 1.04 massmarks and 0.27 calcmarks/image). The following values were reached by all observers without/with CAD in the mean: Sensitivity 80.6/80.0%, specificity 83.2/86.4%, PPV 53.1/58.1%, and NPV 94.6/94.7%. Observers described a similar number of additional lesions without/with the use of CAD (325/326). Whereas the number of unnecessary short-time follow up recommendations increased in all case-subgroups with CAD: 40.8/42.9% (group 1), 35.6/38.1% (group 2), 44.7/46.8% (group 3), respectively, the number of recommended biopsies decreased in all subgroups: group 1: 34.7/27.1%; group 2: 47.4/41.5%, group 3: 33.3/22.0%, respectively.

CONCLUSION

In this rather small population additional usage of CAD led to a lower rate of unnecessary biopsies. The observed decrease of recommended unnecessary biopsies due to the usage of CAD in the screening group suggests a potential financial benefit by using CAD as diagnostic aid.

Influence of breast lesion size and histologic findings on tumor detection rate of a computer-aided detection system

PURPOSE

To evaluate associations between histopathologic findings, tumor size, and detection rate of malignant mammographic findings by using a computer-aided detection (CAD) system.

MATERIALS AND METHODS

The study included 208 mammographically detected histologically proven malignant breast lesions in 208 women. Findings were 150 masses and 114 microcalcifications; 56 lesions showed both findings; 94 lesions, mass only; and 58 lesions, microcalcification only. CAD was used to evaluate mammograms in two views retrospectively. Also, corresponding histopathologic findings and lesion size were evaluated. CAD marks were considered positive if, on at least one view, they correctly identified the corresponding mammographic lesion location.

RESULTS

Ninety percent (135 of 150) of masses and 93.0% (106 of 114) of microcalcifications were marked correctly by the CAD system. Overall tumor detection rate was 93.8% (195 of 208). Size-related detection rate for masses was 83.3% (25 of 30) for lesions up to 10 mm, 100% (45 of 45) for lesions 11-20 mm, 100% (46 of 46) for lesions 21-30 mm, 83.3% (10 of 12) for lesions 31-40 mm, and 52.9% (nine of 17) for lesions larger than 40 mm. Size-related tumor detection rate for microcalcifications was 92.5% (37 of 40) for microcalcifications up to 10 mm, 93.1% (27 of 29) for lesions 11-20 mm, 100% (20 of 20) for lesions 21-30 mm, 87.5% (seven of eight) for lesions 31-40 mm, and 88.2% (15 of 17) for larger microcalcifications. Detection rates for mammographically visible masses (invasive ductal carcinoma, invasive lobular carcinoma, invasive tubular carcinoma, noninvasive cancers, mucinoid cancers, and others) were 92.3% (84 of 91), 89.3% (25 of 28), 75.0% (six of eight), 100% (15 of 15), 33.3% (one of three), and 80.0% (four of five), respectively. Detectability rates for mammographically visible areas suspicious for microcalcifications (invasive ductal carcinoma, invasive lobular carcinoma, invasive tubular carcinoma, and noninvasive cancers) were 92.3% (60 of 65), 100% (eight of eight), 100% (five of five), and 91.9% (31 of 34), respectively. Highest overall detection rates were observed for invasive ductal carcinomas (96.6% [112 of 116]) and noninvasive cancers (92.9% [39 of 42]).

CONCLUSION

Highest detection rates were observed for 10-30-mm tumor masses and for invasive ductal carcinomas and noninvasive cancers.

Computer-aided detection in mammography: an assessment of performance on current and prior images

RATIONALE AND OBJECTIVES

The authors assessed and compared the performance of a computer-aided detection (CAD) scheme for the detection of masses and microcalcification clusters on a set of images collected from two consecutive ("current" and "prior") mammographic examinations.

MATERIALS AND METHODS

A previously developed CAD scheme was used to assess two consecutive screening mammograms from 200 cases in which the current mammogram showed a mass or cluster of microcalcifications that resulted in breast biopsy. The latest prior examinations had been initially interpreted as negative or definitely benign findings (Breast Imaging Reporting and Data System rating, 1 or 2). The study involved images of 400 examinations acquired in 200 patients. Radiologists identified 172 masses and 128 clusters of microcalcifications on the current images. The performance of the CAD scheme was analyzed and compared for the current and latest prior images.

RESULTS

There were significant differences (P < .01) between current and prior images in many feature values. The performance of the CAD scheme was significantly lower for prior than for current images (P < .01). At 0.5 and 0.2 false-positive mass and cluster cues per image, the scheme detected 78 malignant masses (78%) and 63 malignant clusters (80%) on current images. Only 42% of malignant cases were detected on prior images, including 40 masses (40%) and 36 microcalcification clusters (46%).

CONCLUSION

CAD schemes can detect a substantial fraction of masses and microcalcification clusters depicted on prior images. To improve performance with prior images, the scheme may have to be adaptively reoptimized with increasingly more subtle abnormalities.

Soft-copy mammographic readings with different computer-assisted detection cuing environments: preliminary findings

PURPOSE

To assess the performance of radiologists in the detection of masses and microcalcification clusters on digitized mammograms by using different computer-assisted detection (CAD) cuing environments.

MATERIALS AND METHODS

Two hundred nine digitized mammograms depicting 57 verified masses and 38 microcalcification clusters in 85 positive and 35 negative cases were interpreted independently by seven radiologists using five display modes. Except for the first mode, for which no CAD results were provided, suspicious regions identified with a CAD scheme were cued in all the other modes by using a combination of two cuing sensitivities (90% and 50%) and two false-positive rates (0.5 and 2.0 per image). A receiver operating characteristic study was performed by using soft-copy images.

RESULTS

CAD cuing at 90% sensitivity and a rate of 0.5 false-positive region per image improved observer performance levels significantly (P < .01). As accuracy of CAD cuing decreased so did observer performances (P < .01). Cuing specificity affected mass detection more significantly, while cuing sensitivity affected detection of microcalcification clusters more significantly (P < .01). Reduction of cuing sensitivity and specificity significantly increased false-negative rates in noncued areas (P < .05). Trends were consistent for all observers.

CONCLUSION

CAD systems have the potential to significantly improve diagnostic performance in mammography. However, poorly performing schemes could adversely affect observer performance in both cued and noncued areas.

Knowledge-based computer-aided detection of masses on digitized mammograms: a preliminary assessment

The purpose of this work was to develop and evaluate a computer-aided detection (CAD) scheme for the improvement of mass identification on digitized mammograms using a knowledge-based approach. Three hundred pathologically verified masses and 300 negative, but suspicious, regions, as initially identified by a rule-based CAD scheme, were randomly selected from a large clinical database for development purposes. In addition, 500 different positive and 500 negative regions were used to test the scheme. This suspicious region pruning scheme includes a learning process to establish a knowledge base that is then used to determine whether a previously identified suspicious region is likely to depict a true mass. This is accomplished by quantitatively characterizing the set of known masses, measuring "similarity" between a suspicious region and a "known" mass, then deriving a composite "likelihood" measure based on all "known" masses to determine the state of the suspicious region. To assess the performance of this method, receiver-operating characteristic (ROC) analyses were employed. Using a leave-one-out validation method with the development set of 600 regions, the knowledge-based CAD scheme achieved an area under the ROC curve of 0.83. Fifty-one percent of the previously identified false-positive regions were eliminated, while maintaining 90% sensitivity. During testing of the 1,000 independent regions, an area under the ROC curve as high as 0.80 was achieved. Knowledge-based approaches can yield a significant reduction in false-positive detections while maintaining reasonable sensitivity. This approach has the potential of improving the performance of other rule-based CAD schemes.

Applying computer-assisted detection schemes to digitized mammograms after JPEG data compression: an assessment

RATIONALE AND OBJECTIVES

The authors' purpose was to assess the effects of Joint Photographic Experts Group (JPEG) image data compression on the performance of computer-assisted detection (CAD) schemes for the detection of masses and microcalcification clusters on digitized mammograms.

MATERIALS AND METHODS

This study included 952 mammograms that were digitized and compressed with a JPEG-compatible image-compression scheme. A CAD scheme, previously developed in the authors' laboratory and optimized for noncompressed images, was applied to reconstructed images after compression at five levels. The performance was compared with that obtained with the original noncompressed digitized images.

RESULTS

For mass detection, there were no significant differences in performance between noncompressed and compressed images for true-positive regions (P = .25) or false-positive regions (P = .40). In all six modes the scheme identified 80% of masses with less than one false-positive region per image. For the detection of microcalcification clusters, there was significant performance degradation (P < .001) at all compression levels. Detection sensitivity was reduced by 4%-10% as compression ratios increased from 17:1 to 62:1. At the same time, the false-positive detection rate was increased by 91%-140%.

CONCLUSION

The JPEG algorithm did not adversely affect the performance of the CAD scheme for detecting masses, but it did significantly affect the detection of microcalcification clusters.

Identification of clustered microcalcifications on digitized mammograms using morphology and topography-based computer-aided detection schemes. A preliminary experiment

RATIONALE AND OBJECTIVES

A mathematical morphology-based computer-aided detection (CAD) scheme for the identification of clustered microcalcifications was developed and tested. The potential for improving either sensitivity or specificity by combining the results with those previously reported was investigated.

METHODS

The CAD scheme presented here is based on mathematical morphology and a series of simple rule-based criteria for the identification of clustered microcalcifications. A database of 105 digitized mammograms was used for training and rule setting of the scheme. A test set of 191 digitized mammograms was used to evaluate its performance. The same test set had been used to evaluate a multilayer, topography-based scheme. The results obtained by the two schemes were then combined using logical OR and AND operations.

RESULTS

The morphology-based and topography-based CAD schemes performed at sensitivities of 82.9% and 89.5%, with false-positive detection rates of 1.3 and 0.4 per image, respectively. A logical OR operation resulted in 95.4% sensitivity. An AND operation achieved 76.2% sensitivity, with no false identifications on 93% of images.

CONCLUSIONS

By combining the results of the morphology-based and the topography-based schemes, either sensitivity or specificity can be improved.

Computer-aided detection of clustered microcalcifications on digitized mammograms: a robustness experiment

RATIONALE AND OBJECTIVES

The authors assessed the performance of an existing computer-aided diagnosis (CAD) scheme for the detection of clustered microcalcifications in a large image database.

METHODS

A previously developed, rule-based system was used to assess detectability of microcalcification clusters in a set of 386 digitized mammograms with 239 verified clusters visible on 191 images. The test was performed without any reoptimization of the scheme. None of the 386 images had been used in any previous scheme development or testing procedures.

RESULTS

The CAD scheme achieved 89.5% sensitivity at an average false-positive detection rate of 0.39 per image. In 75% of all images, no false-positive findings occurred. Twenty-three of 25 false-negative findings (misses) occurred during the last two stages in the detection process.

CONCLUSION

This scheme produced reasonable results in a large data set of images with a large variety of cluster characteristics.

Adaptive computer-aided diagnosis scheme of digitized mammograms

RATIONALE AND OBJECTIVES

We investigated an adaptive rule-based computer-aided diagnosis (CAD) scheme for digitized mammograms that can be optimized by using an image difficulty index as determined from global measures of image characteristics.

METHODS

First, we defined an image "difficulty" index based on image feature measurements in both the spatial and frequency domains. The CAD scheme then segmented the database into three groups. An image database of 428 digitized mammograms with 220 verified masses was randomly divided into two subsets, one for training (rule-setting) and the other for testing the adaptive CAD scheme. Each of the image difficulty groups in the training set was optimized independently to achieve a low false-positive detection rate while maintaining high detection sensitivity. Scheme performance was then evaluated with the test set, and the results were compared with a global rule-based system that was optimized without the adaptive method.

RESULTS

In this preliminary study, a relatively simple adaptive scheme reduced false-positive mass detections compared with the nonadaptive scheme from 0.85 to 0.53 per image. At the same time sensitivity was not significantly changed.

CONCLUSION

This adaptive CAD scheme has distinct advantages in improving CAD scheme performance as long as the training database includes a large number of cases in each image difficulty group with a variety of true-positive abnormalities.