Mammographic feature analysis of clustered microcalcifications for classification of breast cancer and benign breast diseases

Author

Jiang, Yulei ; Nishikawa, Robert M. ; Wolverton, Dulcy E. ; Giger, Maryellen L. ; Doi, Kunio ; Schmidt, Robert A. ; Vyborny, Carl J.

Author_Institution

Dept. of Radiol., Chicago Univ., IL, USA

fYear

1994

fDate

3-6 Nov 1994

Firstpage

594

Abstract

The authors are developing a computer-aided-diagnosis approach of classifying breast cancer and benign breast disease based on clustered microcalcifications in mammograms. The classification (malignant versus benign) is made by an artificial neural network (ANN) using computer-extracted features of microcalcifications and of clusters as input. The final diagnostic recommendation is made by a radiologist who takes the computer-estimated probability of malignancy into consideration

Keywords

classification; diagnostic radiography; feature extraction; medical image processing; artificial neural network; benign breast diseases; breast cancer classification; clustered microcalcifications; computer-aided-diagnosis approach; computer-estimated probability; computer-extracted features; diagnostic recommendation; malignancy; mammographic feature analysis; medical diagnostic imaging; Artificial neural networks; Biopsy; Breast cancer; Computer networks; Diseases; Feature extraction; Humans; Image databases; Mammography; Spatial databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-2050-6

Type

conf

DOI

10.1109/IEMBS.1994.411886

Filename

411886