Classification of Mammography Images Based on Multifractal Analysis of BIMFs

Abstract

Breast cancer is a real public health problem. Several women with this disease have died from it. Breast cancer is one of the deadliest cancers. Currently, the only way to combat this scourge is the early detection of breast masses. Mammography is a breast x‐ray that allows images of the inside of the breast to be obtained using x‐rays, thereby detecting possible abnormalities. Computer‐aided diagnosis provides significant support in this direction. This work introduces a new system called MF‐BIMFs for computer‐aided diagnosis that automatically analyzes digital mammograms to discover areas of interest in breast images and offers experts a second opinion. This system was based on the combination of two steps. The first step is the image preprocessing which was based on the bidimensional empirical mode decomposition (BEMD) of breast mammographic images, and their objective is to decompose the image into several BIMF modes and the residual, while the second step is the extraction of features and irregularity properties of the preprocessed images from the multifractal spectrum on each BIMF and the residual and to extract a better representation of each mode and provide details capable of differentiating the two healthy and cancerous states, using these properties as characteristic attributes to evaluate their performance in characterizing two conditions objectively. The rate of this classification is given by SVM. The experimental results indicate that the BIMF 1 mode provided the best classification rate, approximately 97.32%. The interest of this new approach was applied to real mammographic image data from the Reference Center for Reproductive Health of Kenitra, Morocco (RCRHKM), which contains normal and pathological mammographic images.