The use of an interactive software program for quantitative characterization of microcalcifications on digitized film-screen mammograms

Downgrading BIRADS 3 to BIRADS 2 category using a computer-aided microcalcification analysis and risk assessment system for early breast cancer

This paper explores the potential of a computer-aided diagnosis system to discriminate the real benign microcalcifications among a specific subset of 109 patients with BIRADS 3 mammograms who had undergone biopsy, thus making it possible to downgrade them to BIRADS 2 category. The system detected and quantified critical features of microcalcifications and classified them on a risk percentage scale for malignancy. The system successfully detected all cancers. Nevertheless, it suggested biopsy for 11/15 atypical lesions. Finally, the system characterized as definitely benign (BIRADS 2) 29/88 benign lesions, previously assigned to BIRADS 3, and thus achieved a reduction of 33% in unnecessary biopsies.

The first trial of phase contrast imaging for digital full-field mammography using a practical molybdenum x-ray tube

RATIONALE AND OBJECTIVES

The image quality of a newly developed full-field digital phase contrast mammography (PCM) system and of a conventional screen-film (SF) mammography system were compared via images of a phantom and receiver operating characteristic (ROC) analysis of clinical images.

METHODS

Magnified (1.75X) PCM images were scanned (sampling rate, 43.75 microm) and then reduced to original-sized, 25-micron pixel images printed on photothermographic film. Along with corresponding SF images, the phantom images were evaluated subjectively, and the clinical images of 38 patients were subjected to ROC analysis of mass and microcalcification.

RESULTS

In the image quality of a phantom, the PCM exceeded the SF. In both mass and microcalcification, the ROC analysis Az values of the PCM clinical images surpassed those of the SF images.

CONCLUSION

The PCM provides better images than the SF. Clinical trials suggest superior detection of both mass and microcalcification by full-field digital PCM over conventional SF mammography.

Accuracy of segmentation of a commercial computer-aided detection system for mammography

PURPOSE

To assess the accuracy of segmentation in a commercially available computer-aided detection (CAD) system.

MATERIALS AND METHODS

Approval for this study was obtained from the authors' institutional review board. Informed consent was not required by the board for this review, as data were stripped of patient identifiers. Two thousand twenty mammograms from 507 women were analyzed with the hardware and software of a commercial CAD system. The accuracy of the segmentation process was determined semiquantitatively and categorized as near perfect if the skin line of the breast was accurately detected, acceptable if only subcutaneous fat was excluded, or unacceptable if any breast parenchyma was excluded from consideration. The accuracy of segmentation was compared for different breast densities and film sizes by using logistic regression (P < .05).

RESULTS

Overall, segmentation was near perfect or acceptable in almost 96.8% of images. However, segmentation defects were significantly more common in mammograms with heterogeneously dense breast tissue (8% unacceptable) than in those with fatty replaced (0% unacceptable), scattered (1.2% unacceptable), or extremely dense (1.8% unacceptable) breast parenchyma (P < .05). For images with unacceptable segmentation, the average percentage of breast parenchyma excluded was almost 25% (range, 5%-100%), with no significant differences among breast densities.

CONCLUSION

For one commercial CAD system, segmentation was usually near perfect or acceptable but was unacceptable more than five times more frequently for mammograms of breasts with heterogeneously dense parenchyma than for those with all other breast densities. On average, one-quarter of the breast parenchyma was excluded from CAD analysis for images with unacceptable segmentation.

Film-screen magnification versus electronic magnification and enhancement of digitized contact mammograms in the assessment of subtle microcalcifications

RATIONALE AND OBJECTIVES

To compare information drawn from magnification mammography with that extracted from electronic magnification, processing, and display of the digitized contact images.

METHODS

Contact and magnification images of a mammographic statistical phantom were obtained. The magnification films versus the computer-enhanced, digitized images of the corresponding contact mammograms were separately presented to three observers. Receiver operating characteristic analysis was used to compare lesion detectability. The contact and magnification mammograms of 86 patients with subtle microcalcifications were also studied. The breast imaging reporting and data system (BI-RADS) scheme was used to compare the magnification patient films versus the corresponding digitized contact images. Differences in mammographic assessment were evaluated by using the kappa statistic. The dose to breast tissue from contact and magnification mammography was measured to evaluate dose reduction in instances where magnification mammography was to be avoided.

RESULTS

Lesion detectability was found to be similar when either the digitized film image or the magnification hard-copy film was inspected. Interpretation of patient images by inspection of the contact and magnification screen-film mammograms on a view-box was in excellent agreement with that yielded by inspection of the contact image on a view-box and the computer-enhanced, digitized contact image on a display monitor.

CONCLUSIONS

Electronic magnification and processing of the digitized contact image may provide valuable information concerning subtle microcalcifications, rendering magnification mammography unnecessary for many patients with such lesions.

Computerized detection of clustered microcalcifications: a modular approach with non-linear filters

The ideal computerized mammogram processing system still needs to be developed. In order to achieve maximum flexibility we suggest a modular scheme, dividing the processing sequence into functionally autonomous modules. This paper provides a general scheme for detection and/or automated recognition of microcalcifications. Some modules that perform ROI selection are introduced, using special non-linear filters designed for microcalcification detection. A first type of filter selects pixels with specific statistical local features, as compared to the local mean. Among these, only pixels satisfying particular constraints on the local standard deviation are kept. Another type of filter then checks the local mean values of gradient components, so that sharp variations, unrelated to small close objects, can be eliminated. The scheme thus applies different non-linear filters in combination, making precise identification of clustered microcalcifications possible. This modular approach seems greatly to simplify system maintenance and consistency, as well as affording a comparison of different processing techniques and parameters.

Quantitative characterization of mass lesions on digitized mammograms for computer-assisted diagnosis

RATIONALE AND OBJECTIVES

To investigate features for discriminating benign from malignant mammographic findings by using computer-aided diagnosis (CAD) and to test the accuracy of CAD interpretations of mass lesions.

METHODS

Fifty-five sequential, mammographically detected mass lesions, referred for biopsy, were digitized for computerized reevaluation with a CAD system. Quantitative features that characterize spiculation were automatically extracted by the CAD system. Data generated by 271 known retrospective cases were used to set reference values indicating the range for malignant and benign lesions. After conventional interpretation of the 55 prospective cases, they were evaluated a second time by the radiologist using the extracted features and the reference ranges. In addition, a pattern-recognition scheme based on the extracted features was used to classify the prospective cases. Accuracy of interpretation with and without the CAD system was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Sensitivity of the CAD diagnosis for the prospective cases improved from 92% to 100%. Specificity improved significantly from 26.7% to 66.7%. This was accompanied by a significant increase in the accuracy of diagnosis from 56.4% to 81.8% and in the positive predictive value from 51.1% to 71.4%. The Az for the CAD ROC curve significantly increased from 0.73 to 0.90. The performance of the classification scheme was slightly lower than that of the radiologists' interpretation with the CAD system.

CONCLUSIONS

Use of the CAD system significantly improved the accuracy of diagnosis. The findings suggest that the classification scheme may improve the radiologist's ability to differentiate benign from malignant mass lesions in the interpretation of mammograms.