## 1 Introduction  
Breast cancer is currently the second most common cause of cancer death among women worldwide, and early detection of breast lesions is the key to increase the chances of survival and reduce the death rate [1; 2]. Although digital mammography is a clinically accepted imaging method for detecting breast cancer in its early stage, there are some difficulties when radiologists analyze mammograms to search for signs of cancer. In particular, Microcalcifications (MCs) are often a primary sign of breast cancer: they are quite tiny calcium deposits and can be distributed in various ways throughout the breast tissue. When MCs are extremely minute (sometimes, they do not exceed 0.1 mm) and occur in clusters, they often indicate the presence of tumor lesions. The identification and characterization of clustered microcalcifications is sometimes a difficult and imprecise process. As a result, missing lesion detection during the routine check or performing many unnecessary breast biopsies on benign microcalcification clusters may occur. Thus, in the screening phase, independent double reading of mammograms by two radiologists is one of the solutions to reduce the missed tumor rate [3]. However, this solution involves a high workload and cost and is time consuming. For these reasons, an automatic tool supporting the radiologist during the mammographic interpretation of such lesions might produce more benefits for the health system with more accurate diagnosis and for the clinics with a lesser radiologist time requirement [4]. Computer technologies and mathematical models can allow increasing the detection rate, and consequently, they can effectively reduce the mortality rate caused by breast cancer [5; 6]. The decision support system known as Computer-Aided Detection/Diagnosis (CAD) may offer radiologists reliable support to detect abnormalities in mammographic images [7; 8; 9]. Indeed, the detection of breast cancer with digital mammography improves when the radiologists can use an artificial intelligence system as the supporting diagnostic tool, without requiring additional reading time [10; 11; 12]. Although various models of CAD systems for breast diseases have been developed in the past two decades, reaching high performances for specific abnormalities, the automatic and accurate classification of microcalcification clusters in benign/malignant lesion is still difficult due to their nature. In fact, while the properties of normal tissue are quite different from abnormal ones, often benign and malignant ROIs may show overlapping characteristics. Some works showed this difficulty in their analysis: they achieved excellent results in terms of the classification of normal tissue and abnormal ROIs, while they were not able to provide an equally good accuracy for the benign and malignant discrimination problem [13; 14]. Furthermore, our previous work on the diagnosis of MC clusters underlined this difference [15]: while the classification of normal and abnormal clusters reached an accuracy greater than 98%, the distinction between benign and malignant MCs required further study, focusing on the extraction of features useful for this purpose. During the last two decades, texture analysis by using a set of local statistical properties of pixel intensity proved to be a useful technique to detect and classify lesions in digital mammograms. The benefit of such an approach results in being more effective when the image is analyzed at different scales: this technique may be performed by decomposing each image into different frequency sub-bands by means of a wavelet transform and calculating first and second order texture features. This is confirmed by the several multi-scale texture analysis approaches recently proposed. The work presented in [16] analyzed a set of features extracted by using wavelet functions based on dual tree complex transforms in order to capture the correlation between different wavelet coefficients and their statistical dependencies.