[The recognition of breast tumor based on ultrasonic image contour features].
The purpose of this article is to evaluate the role of quantitative margin features in the computer-aided diagnosis of malignant and benign solid breast masses using sonographic imaging. The tumour was seperated by the expert. Three contour features circurity (C), area ratio (A) and length width ratio (LWR) was caculated from the tumour contour. Then back-propagation (BP) neural network with contour features was used to classify tumors into benign and malignant. Results from 119 ultrasonic images have been applied in this experiment. BP neural network yielded the following results: 89.7% and 73.5% respectively. The methods applied in this paper are helpful to raise the correctance of breast cancer diagnosis.
Zhang
Kehong
K
Biomedical Engineering Center, Sichuan University, box 373, Chengdu 610065, China.
Peng
Yulan
Y
Li
Deyu
D
Lin
Jiangli
J
Luo
Yan
Y
Wang
Tianfu
T
Jiang
Yinbao
Y
D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D014463:Ultrasonography