Recognition of Breast Ultrasound Images Using A Hybrid Method
Breast cancer is one of the most common cancer in women.A novel method is presented in this paper for classification of the breast tumors as benign or malignant.The method combines k-means classification and a multilayer perception network with error back-propagation (BP)algorithm. The k-means which is an unsupervised classification method is used to get the cluster centers and select the training samples. Only the samples within a specified distance from the cluster centers could be selected as our training samples.The BP Neural Network is used to train the samples and recognize the tumors. The fractal dimension of an ultrasound image of breast tumor is extracted as our feature,which is estimated with the difference between gray values of neighboring pixels by using the fractal Brownian motion.Experiments are done on 125 benign tumors and 110 malignant ones.The recognition rate of the malignant tumors is 94.5％ while that of the benign ones is 93.6％.The result shows that the proposed method can classify the breast tumors effectively.
Breast tumors Fractal dimension k-means classification BP network Hybrid method.
Kai Zheng Tian-fu Wang Jiang-li Lin De-yu Li
Department of Biomedical Engineering,Sichuan University, 610065 Chengdu,Sichuan,China College of Information Engineering,Shenzhen University, 518060 Shenzhen,Guangdong,China Department of Bioengineering,Beijing University of Aeronautics and Astronautics, 100083 Beijing,Chin
国际会议
北京
英文
644-647
2007-05-23（万方平台首次上网日期，不代表论文的发表时间）