## 1 Introduction
However, the majority of CAD-cued false-negative cancers associated with malignant masses (that are overlooked byradiologists in their originally image interpretation) are discarded again by the radiologists as false-positives in the clinical environment [22, 23]. Such errors are primarily caused by (1) the relatively low performance of CAD schemes in mass detection (i.e., the higher false-positive rates [24] and cuing the majority of subtle masses only on one view [25]) and (2) the inability to explain the reasoning of the CAD decision-making (the \"black-box\" type approach). While the clinical benefit of using current commercially available CAD systems is still under debate and test [26, 27], researchers have been actively working on developing a new type of CAD schemes using CBIR approaches [28-36]. Unlike the conventional CAD schemes that detect and cue the suspicious abnormalities based on a \"global\" optimization function trained using the completely available image database, the CAD schemes using CBIR approaches apply an adaptive approach to generate each detection and/or diagnostic result based on the selection of different hypotheses or local approximations as the target function for each unknown query (a suspicious testing region) [37]. Specifically, for each initially automatically detected or manually queried suspicious \"lesion\" depicted on one testing image, the CAD scheme segments the suspicious region and computes the likelihood of this \"lesion\" being associated with true-positive lesion (i.e., cancer) based on comparison with a set of \"similar lesions\" that are retrieved and selected by the CBIR algorithm from the available reference databases. Because the status of all selected references (\"similar lesions\") have been previously verified, researchers have also proposed to develop the intelligent or interactive CAD schemes that aim to provide radiologists \"visual aid\" by displaying both CAD-generated detection (or diagnostic) scores and the selected \"similar\" images on CAD workstations. Preliminary observer performance studies suggested that using this \"visual aid\" tool could improve radiologists' performance in classifying between malignant and benign masses [38] as well as increase radiologists' confidence to accept CAD-cued results (detection scores) in their decision making [39]. Despite of great research interest and the significant progress made in the last several years, developing CAD schemes using CBIR approaches is still in its early stage. Before such a CAD scheme can be routinely accepted and applied in the clinical practice, much research work is still needed. This article takes CAD of breast masses depicted on mammograms as an example to review and discuss several primary issues in developing CAD schemes using CBIR approaches and evaluating their performance and reliability. The discussed development and evaluation concepts should also be applicable to CAD schemes for other types of medical images and diseases. **2. Overview of CAD scheme using CBIR approach**  
Figure 1 illustrates a dataflow diagram of a typical CAD scheme using the CBIR approach. The scheme includes following primary components. First, the scheme either automatically searches for and detects the initial seed of a suspicious region or accepts the queried seed of a suspicious region identified by the human observer (i.e., a radiologist). This step allows the observers to either examine the originally CAD-cued suspicious masses or require (force) CAD to detect and analyze the specific suspicious mass regions that are not initially cued by the CAD scheme. Second, from the detected or queried seed, CAD scheme applies a region growth algorithm to segment the suspicious mass region (defining the boundary contour). The accuracy of suspicious region segmentation determines the accuracy of extracted and computed image features used in CBIR algorithm. Third, the scheme  
extracts and computes a set of image features from the segmented region and its surrounding background.