Original investigationComputerized detection and classification of cancer on breast ultrasound1☆
Section snippets
Materials
Two databases of sonographic images were used in this research. Patients were referred to diagnostic US for various reasons, including young age, dense breast parenchyma with microcalcifications, need for US-guided core biopsy, and for the distinction between cystic and solid lesions in abnormalities detected by mammography. The initial database was obtained from Northwestern University (Chicago, IL), and the second database was collected at the University of Chicago (Chicago, IL), both under
Results
In Figure 1, example images are shown with the computer output, detection points and candidate lesion outlines, together with the outlines by the radiologist for comparison. In the first two examples, the computer detected actual lesions correctly, while in the third example the two lesion candidates identified by the computer are false-positive detections. The comparison of computer outlines with human outlines was the subject of earlier studies (8). Indirectly, however, computer segmentation
Discussion
In this work, we presented a computerized method for detection and classification of lesions on breast US. Evaluation was performed for two schemes to assess whether actual lesions were adequately detected by the computer, and whether our classifier had sufficient potential to distinguish cancers from all other detected lesion candidates. The ability to correctly identify all of the few cancer cases within the testing database was promising, considering the difficulty of the task and
Acknowledgements
The authors thank Dr. Luz Venta and Michael Stern for their help in collecting the sonographic databases.
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This work was supported in parts by USPHS grant CA89452, and U.S. Army Medical Research and Materiel Command 97-2445.
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Drs. Giger and Vyborny are shareholders in R2 Technology, Inc. (Los Altos, CA). It is the University of Chicago Conflict of Interest Policy that investigators disclose publicly actual or potential significant financial interest that would reasonably appear to be directly and significantly affected by the research activities.