## 1 Introduction  
Microcalcifications are defined as calcium deposits inside the breast, which are associated with extra cell activity in breast tissue. If microcalcifications are grouped into clusters, a malignant tumor may develop. Clustered microcalcifications are defined by radiologists as the presence of more than three calcifications in a 1-cm\({}^{2}\) area [1]. Calcifications are seen as bright dots in mammograms with different sizes and shapes, and they can be as small as 100 \(\upmu\)m. Accordingly, detecting microcalcifications is very difficult, and is more complicated in young women due to denser breast tissues, larger low-contrast areas, and highly correlated areas in mammograms. This poses a very challenging task for radiologists. For this reason, good spatial resolution is necessary for the accurate detection of microcalcifications.  
Nowadays, the use of digital mammograms allows computer-aided detection (CAD) software to be used [2; 3; 4; 5; 6; 7]. CAD software helps radiologists during the diagnosis process, which it does almost automatically. The detection process of microcalcifications generally involves the following steps: image enhancement, detection of the region of interest (ROI), feature extraction, and feature selection. In our case, automatic detection is not pursued, but is rather a visual aid for radiologists. Thus, classification and pattern recognition algorithms are not described here.  
There are many methods for detecting microcalcifications [8; 9], and the most commonly used ones are contrast stretching, convolution mask enhancement, local statistical enhancement, adaptive region enhancement, background elimination, morphological processing, wavelet reconstruction, and anisotropic diffusion [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Our method differs from other fractal techniques, which attempt to model the fractal structure in order to identify a target or to remove the background [9; 10; 11; 12]. Our algorithms are based on convolution mask enhancement, and their novelty lies in the use of sets of masks with fractal properties, with maximal gray distribution for a given pixel number and directional properties. This work is a medical application and a development of a previous study [13].  
One main point of our approach is the conservation of the histogram into the zoomed images using the set of fractal masks. This is possible thanks to the maximal distribution of gray pixel values over the mask area for a given number of pixels [13]. The generated masks perform as directional filters to enhance the image in a histogram invariant form. Thus, a complete or directional contrast enhancement of microcalcifications can be performed. This enhanced zooming technique is inspired by Hoffmann microscopy [13].  
Contrast enhancement is performed by calculating the difference between the exponential and the logarithm of the convolved images. Finally, visual segmentation is carried out by the XOR logical operator between both images. Thanks to this visual zooming, enhancement, and segmentation, shapes and sizes of microcalcifications can be analyzed more easily and accurately by radiologists.  
This paper is organized as follows. Section 2 presents the proposed algorithm. Section 3 describes the algorithm implementation and the results. Finally, Section 4 offers the main conclusions.