**2. Background review**

There is a clear need to create effective tools and techniques to search, browse and retrieve images from large repositories to aid diagnoses and research due to the phenomenal growth in recent years in the volume of digital mammograms produced in hospitals and clinical centers. Due to the freely available access to datasets of digital mammograms, such as the Digital Database for Screening Mammography (DDSM), interest in developing CAD schemes for mammograms that use CBIR has been attracting continued research interest during the last several years [20–22]. Although mammography-based CAD is one of the mature and widely adopted fields, there have been only a limited number of studies devoted to CBIR-based CAD systems for the detection and retrieval of breast masses in mammograms. Alto et al. [21] proposed the use of the shape, gradient, and texture features for mammography image retrieval and that was one of the earliest researches on CBIR for mammograms. Linear discriminant analysis, logistic regression, and the Mahalanobis distance were used to evaluate the features for classifying the masses. Kinoshita et al. [22] used the breast density to retrieve images from a mammogram dataset available at the Clinical Hospital of the University of São Paulo at Ribeirão Preto, Brazil. Shape, texture features, moments, Radon transform, and histograms were used to describe breast masses, and the Kohonen self-organizing map (SOM) neural network was used for image retrieval. Wang et al. [27] has utilized histograms for the characterization of breast mass in a set of mammogram database at the Medical Center of Pittsburgh in order to automatically evaluate breast mass. They obtained 71% of correct classification rate with the use of a neural network. Muramatsu et al. [28] proposed a psychophysical similarity measure based on neural networks for evaluation of similar images with mammographic masses. The major drawback is that a large amount of data is required to train an artificial neural network (ANN). Oliveira et al. proposed a CBIR system called MammoSys; the novelty of this study is to present a two-dimensional principal component analysis (2DPCA) method [29] for the description of mass texture and thereby also a dimensionality reduction is performed. Wei et al. [30] proposed an adaptive classification scheme in the context of SVM assisted by content-based image retrieval to improve the classification accuracy in the computer aided diagnosis for breast cancer. A CBIR scheme is proposed in [31] that utilizes SVMs capable of optimally exploiting the distribution of input samples in the feature space on the basis of BI-RADS classifications of masses as carried out by the radiologists. In an article by Zhang [20], a number of CBIR-based CAD schemes for mammograms were compared and their performance were assessed and it was concluded that much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.
