## 1 Introduction
This step aims to search for and identify effective image features that should reduce the \"semantic gap\" between computer vision and human vision. Fourth, the scheme may early discard a substantial fraction of images stored in the reference image database. Although the performance and robustness of all CAD schemes heavily depends on the size and diversity of reference databases, the CAD schemes using CBIR approaches use an adaptive and \"local\" approximation method in which only a small number of references have direct impact on the detection or classification of the specifically queried ROI when using the CBIR algorithm. Thus, early discarding the majority of reference ROIs with relatively lower correlation to the queried ROI is also an important step to improve the computational efficiency of the CAD scheme. Fifth, the scheme applies a CBIR algorithm to compare and retrieve a set of \(K\) reference images (or ROIs) that are considered the \"most similar\" to the queried mass (or ROI). The searching and retrieving result of the CBIR algorithm depends on the effectiveness of the summary index or criterion (i.e., distance metric) to measure the \"similarity\" level among the selected images. Sixth, based on the similarity levels of the retrieved reference ROIs to the queried ROI and their verified outcomes (i.e., either positive or negative), CAD scheme computes a likelihood (detection or classification) score of the queried ROI being associated with a positive (or malignant) mass. Figure 1: Illustration of a CAD scheme using the CBIR approach. Unlike the conventional CAD schemes (i.e., the current commercialized CAD) that only highlight the locations of the detected suspicious regions depicted on each image, the CAD schemes using CBIR approaches can typically display CAD-generated detection scores along with a set of similar images or ROIs on the CAD workstation. For example, when applying an interactive CAD scheme to process one image (Figure 2), the CAD scheme automatically detects one suspicious mass region. If the detected region is queried by the observer, the CAD scheme applies all processing steps as shown in Figure 1 to analyze this queried ROI. Finally, the scheme will provide observers two likelihood scores and a set of the \"most similar\" reference ROIs depicting suspicious masses with verified outcome. Figure 2 shows the location and automatically segmented boundary contour of the queried ROI depicted on the image (mammogram), a set of 12 \"similar\" ROIs selected by the CBIR algorithm, and two likelihood scores. The detection score of 0.96 indicates that the likelihood of this region representing a true-positive mass region is 96%, while the classification score of 0.74 represents that the likelihood of this region being malignant is 74% (if this is a true-positive mass). The detection score is computed using the similar approach of the conventional CAD schemes (i.e., a globally pre-optimized artificial neural network (ANN) [40]) in the first step of the CAD scheme before the region is queried (Figure 1). The classification score (_PTP_) is typically computed based on the distribution of similarity levels (_w_) of \(K\) reference images (ROIs) selected by the CBIR algorithm using either one of the following two equations:  
\[P_{TP}=\frac{\sum\limits_{i=1}^{N}w_{i}^{TP}}{\sum\limits_{i=1}^{N}w_{i}^{TP} +\sum\limits_{j=1}^{M}w_{j}^{FP}}\,,\ N+M=K\, \tag{1}\]  
\[P_{TP}=\frac{1}{N}\sum\limits_{i=1}^{N}w_{i}^{TP}-\frac{1}{M}\sum\limits_{j=1 }^{M}w_{j}^{FP}\,,\ N=M=\frac{K}{2}\, \tag{2}\]  
where \(N\) is the number of malignant mass regions (TP) and \(M\) is the number of non-cancer regions (FP) including ROIs depicting either benign masses or CAD-cued false-positive regions [41]. Using the CBIR-based CAD scheme as demonstrated in Figure 1, radiologists can query any suspicious mass regions depicted on the testing images and view both CAD-generated results (the detection score) and CBIR-generated results (a set of similar reference images or ROIs) for each queried ROI.