Detection and segmentation of microcalcifications in digital mammograms using multifractal analysis

The aim of this study is the detection and segmentation of microcalcifications in digital mammograms using multifractal analysis. To detect the suspicious Region Of Interest (ROI), containing anomalies, we propose to decompose the whole image into ROIs and compare the multifractal spectrums based on the q-structure functions of each one. The segmentation of microcalcifications consists of two steps. On the first step, we create an image denoted `α_image´. This image is constructed using the singularity coefficient, deduced from multifractal spectrum. Then, in the next step, we enhance the visualization of microcalcifications by creating an image denoted `f(α)_image´ based on the global regularity measure of the `α_image´ spectrum. We investigated the robustness of our approach using a data set of mammograms from `MiniMIAS´ database. Results demonstrate the accuracy of our approach, which successfully detect and segment microcalcifications with irregular form and small size.