Detection of architectural distortion in mammograms based on relevance vector machine
Shengjun Zhang · Journal of Optoelectronics·laser · 2013
Detection of architectural distortion(AD) in mammograms is one of important approaches in breast cancer diagnosis.Using support vector machine(SVM) to detect AD can achieve high accuracy rate,but it is also with slow speed,making it not suitable for clinical application.To solve the above problems,a method to detect AD in mammograms based on relevance vector machine(RVM) is proposed.Firstly,the discrete wavelet transform is applied to extract features in region of interest(ROI).Then the cross validation(CV) method is used to determine the optimum type and parameters of RVM kernel function.Lastly,RVM is applied to classify the test samples to obtain the detection results of AD.The proposed method is evaluated on mammograms from the mammographic image analysis society(Mini-MIAS) and those from the breast cancer of Peking University People′s Hospital.The results show that compared with SVM method,the proposed method achieves essentially the same sensitivity with much higher speed of detection,which can shorten the detection time of AD more than 90%.The proposed method is more applicable for mammograms with different characteristics of both oriental and occidental women.