• 2018-07
  • 2020-07
  • 2020-08
  • br Third unlike many previously developed CAD schemes that f


    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 ZD1839 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.
    Fourth, since identifying optimal and non-redundant images features is one of the most important and challenged tasks in developing the conventional machine learning classifiers including the SVM classifier [22], we in this study investigated advantages of applying a PSO method to select optimal features and guide the process of training SVM classifier. The results demonstrated that this method enabled to identify and combine the useful pattern inside the global mammographic images features while removing the redundancy. Thus, using this optimal feature selection method, we are able to use a relatively small dataset of 275 cases for the SVM model training and optimization, which avoids the large database requirement when developing the deep learning based CAD schemes.
    Despite the encouraging results, we recognize that this study has the following limitations. First, the database established in this investigation was only from one institution with a limited number of cases; therefore, a more diversified dataset including the cases from more than one institution would be desirable to further test the reliability and robustness of our proposed scheme. Second, the initial feature pool with 59 features used in this study may not be an optimal feature pool. We should expand the feature pool using a list of ZD1839 functional and diversified features
    summarized in the literature [30]. Meanwhile, codominance is also worth investigating different feature selection methods such as Relief [31], recursive feature elimination [32], variable ranking techniques [33], supervised training [34] or combining them with our PSO-SVM strategy. Third, this preliminary study used the supervised SVM classifiers that have strengths of solving complex problems and adapting well to high dimensional data (or feature vector); however, there may exist necessities to explore other effective classifiers (e.g. linear discriminant analysis (LDA) and artificial neural networks (ANNs)), in particular the fusion of multiple classifiers to loosen the data size requirement [35] and balance the computational efforts to resolve the uncertainty of the model [29]. Last, similar to our previous study, which demonstrated that fusion of the complementary information of global (case-based) and regional (lesion-based) mammographic image features had potential to significantly improve CAD performance in detecting suspicious lesions without increase of false-positive rates [34], we will investigate the optimal fusion method to fuse the classification results of this global feature based scheme with the lesion-based scheme [35] to more accurately and robustly classify between malignant and benign mammographic cases in the future.