The Application Of Ultrasonic Elastography In The Diagnosis Of Breast Tumors

Breast cancer is one of the most common malignancies in women,and its incidence rate is the first place in the women malignant tumor,and shows a rising trend.Therefore,diagnosis and prevention of breast cancer is given great attention to the medical field.The report shows that the majority of early breast cancer can be cured,its five-year survival rate can reach over 90%,so early detection and early treatment becomes very necessary.In recent years,the mortality of breast cancer is decline.The reason is that the application of ultrasonic elastography technique and computer-aided diagnosis,and identify the potential mass more effective,and greatly improve the diagnostic accuracy of breast disease,and has great significance for the cure of breast cancer.This paper studies the diagnostic method of the ultrasonic elastography technique in breast tumors classification.Breast mass is the most common symptoms of breast tumors,therefore,this article extracts the elasto graphic features and the texture features of the masses,which is an important basis for distinguishing the benign and malignant breast masses.The research methods:firstly,acquire the image data,and complete the pre-processing of the breast images;secondly,reconstruct the elasticity information of the masses;thirdly,apply the level sets method to achieve the automatic segmentation of the tumor;fourthly,achieve the feature extraction of the masses,this article extract five elastographic features to describe the properties of tumor according to the elasticity information of the breast masses;extract four GLCM features according to the texture features of the breast masses;extract the roundness feature according to the shape of the tumor;extract a total of ten features to be used as basis of mass classification;fifthly,SVM is studied,including theoretical foundation of SVM,introduction of the kernel function,and the SVM technology is applied to classification of breast masses,and to complete the diagnosis of breast masses.195 breast images are diagnosis by above method,training and identifying SVM by using cross-validation method,in order to make the results of SVM classification with high reliability,and the significant experimental results are achieved,which lay a good foundation for further studies.