Morphological detection and neuro-genetic classification of masses and calcifications in mammograms for computer-aided diagnosis

Diagnosis of breast cancer is the main worry of oncologists of this era, which knows an anxiogenic increase of the incidence in the world. This paper is destined for the semi-automatic detection of breast neoplasm taken, from digital mammograms of MIAS database (Mammographic Image Analysis Society). This research is focusing on analysis of masses and, calcifications. Therefore, the first phase of the system consists, on pre-processing of pathological structures, by morphological transformations in order to refine, the segmentation. The second step, realises extraction of clinical signs, according to adaptive deformable model which initialisation is guided by, the annotated suspicious zone. The third block is to characterise abnormalities, by morphometric and textural attributes, to generate their signature. The ultimate systemic description, categorises malignant and benign masses and calcifications from their knowledge, by a neuro-genetic classifier for computer-aided diagnosis. The elaborated decisional system, products, an accuracy of 99.25%, for the shape recognition.