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  • Doxorubicin br have been previously developed


    have been previously developed and reported in the literature (i.e., [10, 14]), these schemes computed image features from a fixed region of interest (ROI) covering the suspicious lesions, which have disadvantages or difficult to adaptively identify the optimal size of the ROIs to cover the lesions with varying size and shape. The approach in this study is different. Thus, to the best of our knowledge, this is the first study that investigate the feasibility of developing a global breast image feature-based CAD scheme to classify between malignant and benign mammographic cases, which avoid difficulty in both segmentation of the lesions and determination of the optimal ROIs, which are the two popular approached used in previous studies.
    Second, we trained and tested two SVM classifiers using two feature pools containing the global images features computed from two-view images of one positive breast and four-view images of two breasts. The testing results show that the SVM classifier yielded AUC of 0.79±0.07 when two-view images of one positive breast were involved in the training and testing process. However, when using the image features computed from four-view images of two breasts to build the SVM classifier, the scheme yielded a reduced performance with an AUC of 0.75±0.08, which implies that the discriminatory information or power may be diluted when adding two negative images of one cancer-free breast. Thus, it Doxorubicin should be better to use two-view images of one breast to train the SVM classifier. Then, CAD scheme can be applied to two-view images of left and right breasts separately. The higher classification score should be selected to represent the likelihood of the testing case being malignant.
    Third, unlike many previously developed CAD schemes that focus on computing the morphological and density distribution based features in the spatial domain, we computed image features in both spatial domain (Shape&Density group) and frequency domain (FFT, DCT, Wavelet block-based groups). As shown in Table 3, the top 10 performed image features in two feature pools including the features computed from two-view and four-view images contain image features computed in both spatial and frequency domains. For example, among the six common features computed from both two-view and four-view images, two are spatial domain features (i.e., MeanGradient, StdGradient) and four are frequency domain features (i.e., MeanDeviation_DCT, Energy_DCT, , Energy_FFT, Mean_DCT). This result shows that the copious lesion pattern information exists in both spatial and frequency domain, which was also indicted in our previous investigation of assessing response of the metastatic tumors to
    chemotherapy using CT images [29] and verified in this study for classification between malignant and benign mammographic image cases. In addition, we also observed that the top three features are totally different between two-view and four-view image predictions: The top three features computed from two-view images of positive breasts with the suspicious lesion detected are MeanGradient, MeanDeviation_DCT, and Mean_FFT, and the top three features computed from four-view images of two breasts are Energy_FFT, Energy_DCT and Mean_Density. This difference may be due to the nature of the two-view and four-view images. As verified in this study, the normal tissues on the mammogram also contain clinically descriptive information for mass classification. However, the normal and abnormal tissues depict significantly different properties on the mammograms, thus different types of features are needed to identify and collect the relevantly useful information from both normal and abnormal the tissue structures. Since two view images contain positive masses, the top features should have a balanced capability to collect the discriminative information from both the masses and the normal tissues. For the four view images, given that two images are completely normal, the selected features should have a better capability to extract the discriminant characters from the normal tissues.