摘要
通过对乳腺肿瘤边界特征的分析,得到边界的特征量似圆度,面积比率,长宽比组成的特征矢量,最后用反向传播人工神经网络(BP)的算法对经病理证实的119幅乳腺良、恶性肿块超声图像进行分类识别。BP神经网络对良、恶性肿瘤正确识别率分别为89.7%、73.5%。量化后的乳腺超声图像肿瘤轮廓特征结合BP神经网络可以比较有效的用于肿瘤的良、恶性识别。
The purpose of this article is to evaluate the role of quantitative margin features in the computeraided diagnosis of malignant and benign solid breast masses using sonographic imaging. The tumour was seperated by the expert . Three contour features eireurity(C) ,area ratio(A) and length width ratio(LWR) was eaeulated from the tumour contour. Then baek-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 eorrectanee of breast cancer diagnosis.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2006年第6期1237-1240,共4页
Journal of Biomedical Engineering
基金
四川省青年科技基金资助课题(05ZQ026-019)
四川省应用基础研究项目资助(03JY029-072-2)