DocumentCode :
1819965
Title :
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
Link To Document :
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