Classification of breast mass in mammography using anisotropic diffusion filter by selecting and aggregating morphological and textural features

In this paper, we describe a novel mammogram classification framework for classifying breast tissues as normal, benign or malignant. First, we establish a set of symbolic rules for mammograms pre-classification from the particularities of a tissue (the background, class and severity of abnormality) taking into account the different forms of breast according to their abnormality. A decision tree reflecting a preliminary attribution from the region of interest (ROI) of a mammogram represents this pre-classification. The images obtained by this technique are processed with an anisotropic diffusion filter to reduce the noise and preserve edges. Then, a feature matrix is generated using: on the one hand, textural features from both a Gray Level Co-occurrence Matrix (GLCM) and a Gray Level Run Lengths Matrix (GLRLM) applied to all the filtered regions of interest (ROI) of a mammogram; and on the other, after mass region is segmented out, the features extracted from the mass boundary and the margin region between the mass and background for classification. To derive the relevant features from the feature matrix, we resort to RELIEF and MRMR feature selection method separately. The key point of our proposal is the modeling of Back Propagation Neural Network (BPNN) by a network of queues for representing the textural and morphological properties of the masses. The relevant and ordered features are injected in BPNN classifier using queuing network. The standard database MIAS is used for validation of the proposed scheme. In any case, we observed that the opening of closed neural network improves the performance. However, the MRMR feature selection method outperforms RELIEF one the point of view of precision and area under curve of receiver operating characteristic. These measures are respectively 98.1% and0.9650 for normal vs. abnormal classification, whereas, they are respectively 95.2% and 0.9200 for benign vs. malignant classification.