Automated classification of mammographic microcalcifications by using artificial neural networks and ACR BI-RADS criteria

Takeshi Hara; Akitsugu Yamada; Hiroshi Fujita; Takuji Iwase; Tokiko Endo

doi:10.1117/12.431067

3 July 2001 Automated classification of mammographic microcalcifications by using artificial neural networks and ACR BI-RADS criteria

Takeshi Hara, Akitsugu Yamada, Hiroshi Fujita, Takuji Iwase, Tokiko Endo

Author Affiliations +

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431067
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

We have been developing an automated detection scheme for mammographic microcalcifications as a part of computer-assisted diagnosis (CAD) system. The purpose of this study is to develop an automated classification technique for the detected microcalcifications. Types of distributions of calcifications are known to be significantly relevant to their probability of malignancy, and are described on ACR BI-RADS (Breast Imaging Reporting and Data System) , in which five typical types are illustrated as diffuse/scattered, regional, segmental, linear and clustered. Detected microcalcifications by our CAD system are classified automatically into one of their five types based on shape of grouped microcalcifications and the number of microcalcifications within the grouped area. The type of distribution and other general image feature values are analyzed by artificial neural networks (ANNs) and the probability of malignancy is indicated. Eighty mammograms with biopsy-proven microcalcifications were employed and digitized with a laser scanner at a pixel size of 0.1mm and 12-bit density depth. The sensitivity and specificity were 93% and 93%, respectively. The performance was significantly improved in comparison with the case that the five criteria in BI-RADS were not employed.

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Takeshi Hara, Akitsugu Yamada, Hiroshi Fujita, Takuji Iwase, and Tokiko Endo "Automated classification of mammographic microcalcifications by using artificial neural networks and ACR BI-RADS criteria", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431067

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