## 4 Feature Selection  
By definition, CBIR is the process of retrieving related (or similar) images from the large databases using their image content. Since applying CBIR algorithms to search for similar images (or ROIs) by simply comparing large sets of pixels between a query image and the reference images in the database is not only computationally expensive but is also very sensitive to and often adversely impacted by noise in the image or changes in views of the imaged content [61], many of effective pixel value based similarity measures for template matching or image registration [62] are not suitable to be used in the CAD schemes. In the CAD applications, the image content should be represented by a set of extracted image features that meet following two criteria. First, since a highly performed CAD scheme using CBIR approach should not only achieve the high performance in detecting suspicious masses (measured by the area under ROC curve), but also generates smaller \"semantic gap\" between human vision (the high-level image scene understanding) and computer vision (the low-level pixel based image analysis) [63]. Thus, the CAD scheme should include at least some of the features that are closely correlate with visual similarity in a multi-feature based CBIR algorithm. Second, because the clinically and visually similar lesions or disease patterns can depict on different sections of the examined organ (the locations of medical images) with different orientations, the selected features should be invariant to the linear shift and rotation of the targeted lesions. For the potentially clinical utility, this criterion is very important. Since when applying CBIR algorithms in the CAD scheme, the requirement in the computational efficiency does not allow scheme spending significant time to optimally align the lesions depicted on the images. As a result, if the selected features or similarity measurement indices do not meet this criterion, CBIR algorithm may generate a quite lower similarity score between two actually similar lesions if they have different orientations and thus discards this similar reference image. Many morphological features computed from the segmented mass regionsmeet these two criteria. For example, one CBIR algorithm used in the CAD scheme selects a set of the \(K\) \"most\" similar mass regions that all have the similar or comparable size, circularity, and boundary margin spiculation level to the queried mass regions [41]. In selecting effective features used in CBIR schemes for medical images, great research efforts have been focused on identifying and extracting the better features to capture the texture of images and improve correlation to the human visual similarity. Among them, wavelets and Gabor filters have been extensively investigated and compared [64; 65] in which study found that Gabor filters performed better and corresponded well to the human vision (in particular for the sensitivity of edge detection) [66]. Other popular texture features derived from the co-occurrence matrices [67] and Fourier transformation [68] have also been tested in developing CBIR schemes. Based on the analysis of medical domain knowledge, one recent study reported that the similarity evaluated based on the combination of four types of image features (color histogram, image texture, Fourier coefficients, and wavelet coefficients) using the feature vector dot product as a distance metric was correlated well with the observed visual similarity [63]. Recently, another study reported that one image feature (namely, the fractal dimension) could be more effectively and efficiently used as an objective index or quantitative measure to assess and control the texture similarity of reference ROIs selected by the CBIR algorithms without reducing CAD performance in detecting and classifying suspicious breast mass regions [69].