Diagnosis of masses in mammographic images based on Zernike moments and local binary attributes

Masses are important elements in the diagnosis of breast cancer. Many studies discussed the problem of detection and/or diagnosis of masses and most of these researches were based on shape descriptors to make decision. Textural descriptors contribute in indicating the presence of masses. Morphological descriptors determine their malignancy degree. Thus, we decided in our work to make a combination of morphological and textural descriptors. In fact, this method allowed us to extract different features in order to help make a decision concerning the malignancy of masses. The shape descriptor “Zernike moments” has the advantages to be invariant to the rotation and to be orthogonal. In addition, the texture descriptor “local binary attributes” provides information about the local variations of gray levels in the image. A multi-layer perceptron is used in the classification stage. The results were validated by using 160 regions of interest which are extracted from the database of mammographic images DDSM (Digital Database for Screening Mammography). We obtained an area under the ROC (Receiver Operating Characteristics) curve which is equal to 0,96. The results were confirmed by a radiologist.